The Unified Memory System & Agent Master Loop v3.3: A Comprehensive Analysis of an Advanced Cognitive Architecture

I. Introduction: Bridging Cognition and Engineering

The system presented represents a significant leap forward in AI agent design, architecting a synergistic relationship between two core components:

1. Unified Memory System (UMS): A sophisticated, persistent knowledge base and state tracker inspired by human cognitive memory models, built on a robust database foundation.
2. Agent Master Loop (AML) v3.3: A central executive orchestrator that leverages the UMS, guides an advanced LLM (like Claude 3.7 Sonnet) through complex tasks, and implements powerful meta-cognitive and adaptive capabilities.

Together, they form an integrated cognitive architecture designed for autonomous, adaptive, and reflective AI agents capable of complex reasoning, planning, learning, and long-term task execution. It moves beyond simple memory retrieval or reactive loops to enable more nuanced and robust agent behavior.

II. The Unified Memory System (UMS): The Agent's Cognitive Foundation

The UMS is far more than a simple database; it's the structured repository of the agent's knowledge, experience, and operational history.

• Human-Inspired Memory Hierarchy: Drawing directly from cognitive psychology, the UMS implements a multi-level memory structure with distinct characteristics and default Time-To-Live (TTL) parameters:

  • Working Memory: Small, active set for immediate reasoning (short TTL, e.g., 30 min). Managed via cognitive_states table.
  • Episodic Memory: Records of specific events, actions, observations (medium TTL, e.g., 7 days). Often linked to actions or artifacts.
  • Semantic Memory: Generalized facts, insights, summaries (long TTL, e.g., 30 days). Often results from consolidation or reflection.
  • Procedural Memory: Learned skills, methods, multi-step procedures (very long TTL, e.g., 90 days).
  • Mechanisms: Importance, confidence scores, access counts, and a configurable decay rate (MEMORY_DECAY_RATE) influence memory relevance and persistence (_compute_memory_relevance).

• Rich Content Classification: Memories are further categorized by MemoryType (Observation, Fact, Insight, Plan, Question, Summary, Reflection, Skill, Procedure, etc.), creating a fine-grained taxonomy for precise management and retrieval.

• Robust Database Schema: Implemented in SQLite (using aiosqlite for async operations), the schema meticulously tracks:

  • workflows: High-level goals and overall state.
  • actions: Discrete steps with status, tool usage, results, and dependencies.
  • artifacts: Tangible outputs (files, data, text) linked to actions.
  • thought_chains & thoughts: Structured reasoning sequences, linking thoughts to actions, artifacts, and other memories.
  • memories: The core knowledge units with levels, types, metadata, and links.
  • memory_links: Explicit, typed relationships (Related, Causal, Supports, Contradicts, etc.) forming an associative graph.
  • embeddings: Vector representations for semantic search (using configurable models like text-embedding-3-small).
  • cognitive_states: Snapshots of the agent's focus and working memory.
  • reflections & memory_operations: Logs for meta-cognition and system auditing.
  • tags: Flexible categorization across entities.
  • Optimizations: Leverages WAL mode, indexing, FTS5 for keyword search, memory mapping, and custom SQLite functions for performance.

• Sophisticated Memory Operations (Exposed via Tools):

  • Storage (store_memory): Creates memories with metadata, generates embeddings, and proactively suggests semantic links based on similarity.
  • Retrieval (query_memories, search_semantic_memories, hybrid_search_memories, get_memory_by_id, get_linked_memories): Offers flexible retrieval via structured filters, pure semantic search, keyword search (FTS5), and a powerful hybrid approach combining semantic and keyword/relevance scores. Allows traversal of the memory graph.
  • Linking (create_memory_link, background _run_auto_linking): Enables explicit creation of typed links and automatic background linking based on semantic similarity.
  • Management (update_memory, promote_memory_level, delete_expired_memories): Allows modification of memories (optionally regenerating embeddings), promotion between cognitive levels based on usage/confidence heuristics, and cleanup of expired knowledge.
  • Working Memory (get_working_memory, focus_memory, optimize_working_memory, auto_update_focus): Provides tools to inspect, prune, and manage the agent's active attentional set.

• Workflow & Reasoning Tracking:

  • Manages workflows, hierarchical actions with dependency tracking (add_action_dependency), and associated artifacts (record_artifact).
  • Supports multiple, named thought_chains (create_thought_chain) for modular reasoning, with thoughts explicitly logged (record_thought).

• Meta-Cognitive Support (consolidate_memories, generate_reflection, summarize_text, compute_memory_statistics): Provides tools for the agent (or external processes) to synthesize knowledge, reflect on performance, compress information, and gather statistics for adaptation.

III. The Agent Master Loop (AML): Orchestrating Intelligent Behavior

The AML acts as the agent's central executive, driving goal achievement through a cycle that leverages the UMS and an LLM's reasoning power.

• Core Orchestration Cycle:

  1. State Management: Loads/saves AgentState (plan, workflow context, meta-cognitive counters, adaptive thresholds).
  2. Context Gathering (_gather_context): Assembles a rich, multi-faceted context for the LLM, going far beyond simple chat history. This includes: core workflow details, current working memory, proactively retrieved memories relevant to the plan (using hybrid search), relevant procedural memories, summaries of linked memories, error history, meta-cognitive feedback, and the current plan. It dynamically checks context size and can trigger compression (summarize_text).
  3. LLM Decision (_call_agent_llm): Constructs a detailed prompt instructing the LLM on its capabilities, the available UMS tools, and the need for structured reasoning, planning, error handling, dependency awareness, and explicit memory management. The LLM analyzes the context and outputs:
     • A rationale/thought process.
     • An Updated Plan (as structured JSON PlanStep objects).
     • A single action decision (call a specific tool, record a thought, or signal completion).
  4. Plan Update: Prioritizes the LLM's structured Updated Plan. If unavailable or invalid, falls back to heuristic plan updates (_update_plan) based on action success/failure.
  5. Action Execution (_execute_tool_call_internal): Robustly handles tool calls: finds the server, checks prerequisites based on the plan's depends_on field using get_action_details, records action start/completion/dependencies, executes the tool, handles results/errors, and triggers background tasks like auto-linking or memory promotion checks.
  6. Periodic Meta-Cognition & Maintenance (_run_periodic_tasks): Runs background tasks at intervals or based on triggers (e.g., errors, action counts relative to dynamic thresholds):
     • Reflection: Analyzes recent operations (generate_reflection) providing feedback.
     • Consolidation: Synthesizes memories (consolidate_memories).
     • Optimization: Manages working memory (optimize_working_memory, auto_update_focus).
     • Promotion: Checks eligibility and elevates memories (promote_memory_level).
     • Maintenance: Cleans up expired data (delete_expired_memories).
     • Statistics & Adaptation (compute_memory_statistics, _adapt_thresholds): Gathers memory usage statistics and dynamically adjusts the thresholds for reflection and consolidation, making the agent's self-management adaptive.

• Structured Planning (PlanStep, _check_prerequisites): Implements planning using explicit PlanStep objects with status tracking and, crucially, depends_on fields that are actively checked before execution, enabling more reliable complex task handling.

• Error Handling & Robustness: Tracks consecutive errors, provides detailed error context to the LLM, and uses reflection/replanning mechanisms for recovery. Dependency checks prevent cascading failures.

• Thought Chain Management: Explicitly creates (create_thought_chain) and tracks (current_thought_chain_id) reasoning threads, allowing for focused sub-problem solving or exploration of alternatives.

IV. Key Innovations and Differentiators

This architecture distinguishes itself significantly from typical agent frameworks and published literature:

1. Deep Cognitive Integration: It doesn't just bolt on a vector DB. Cognitive principles (memory levels, decay, confidence, linking, promotion, working memory) are woven into the UMS structure and the AML's operational logic.
2. Agent-Driven Meta-Cognition via Tools: The agent actively manages its own knowledge base and reflects on its processes using specific tools (generate_reflection, consolidate_memories, store_memory, update_memory). This goes beyond passive memory access.
3. Adaptive Self-Management: The dynamic adjustment of meta-cognitive thresholds (_adapt_thresholds) based on operational statistics is a novel form of self-regulation, allowing the agent to modulate its cognitive load.
4. Structured Planning with Explicit Dependencies: The PlanStep model combined with active prerequisite checking (_check_prerequisites) provides a robustness and capability for complex sequencing often missing in LLM agents.
5. Rich, Multi-Faceted Context: The deliberate gathering of diverse information (working memory, proactive semantic/procedural memories, core history, errors, feedback) provides superior situational awareness for the LLM.
6. Hybrid Search & Sophisticated Retrieval: Offering multiple search strategies (semantic, keyword, hybrid) plus structured filtering and link traversal provides nuanced and powerful information access.
7. First-Class Thought Chains: Treating reasoning threads as manageable entities (thought_chains, create_thought_chain) supports more complex and organized cognitive processes.
8. Explicit Working Memory Model: Implementing a distinct, managed working memory allows for a more realistic simulation of attentional focus.
9. Comprehensive Traceability & Reporting: Detailed logging across all system components (workflows, actions, thoughts, memory operations) coupled with visualization and reporting tools (generate_workflow_report, visualize_memory_network, visualize_reasoning_chain) enables deep analysis and debugging.
10. Holistic Integration: The true innovation lies in the synergy – how the UMS's rich structure enables the AML's advanced planning, context gathering, meta-cognition, and adaptation.

V. Comparison to Literature

• Vs. Simple Memory Frameworks (Vector DBs, Buffers): Offers vastly superior structure, cognitive features (levels, decay, linking), explicit management tools, and integration with workflow tracking.
• Vs. RAG (Retrieval-Augmented Generation): RAG typically focuses on augmenting prompts with external data. This system manages an internal, evolving cognitive state and uses retrieval as one part of a broader cognitive cycle.
• Vs. ReAct/Chain-of-Thought: These focus on interleaving thought and action within a prompt context. This system externalizes memory and planning into persistent, structured components managed by the agent over long durations.
• Vs. Basic Agent Frameworks (LangChain, LlamaIndex): While offering components, they generally lack this level of deep integration, opinionated cognitive architecture, built-in adaptive meta-cognition, and robust dependency management. This system is a more complete cognitive architecture.
• Vs. AutoGPT/BabyAGI: These early autonomous agents had simpler memory, less robust planning, and lacked the sophisticated meta-cognitive and adaptive capabilities.
• Vs. Cognitive Architectures (ACT-R, SOAR): Adapts principles from these architectures into a practical framework integrated with modern LLMs and tool use, focusing on agent autonomy rather than detailed human cognitive simulation.

VI. Significance and Potential (Towards AGI / Publishability)

• Towards AGI: While not AGI itself, this system represents a significant architectural step forward. It addresses key challenges like persistent and organized memory, structured reasoning, contextual awareness, self-management, and robustness – essential building blocks for more generally capable AI. The adaptive meta-cognition is particularly relevant to creating systems that learn and improve autonomously. It provides a powerful "cognitive operating system."
• Publishability: Yes, this work is highly publishable. Its novelty lies in the integrated architecture, the agent-driven adaptive meta-cognition, and the combination of cognitive principles with robust engineering. A strong paper would detail the architecture, contrast it with related work, and, critically, provide empirical evidence (through well-designed tasks, baselines, ablation studies, and metrics) demonstrating the benefits of its features (e.g., improved task success, robustness, efficiency, learning) compared to simpler agent designs.

VII. Conclusion: A Blueprint for Advanced Agents

This system is more than just an agent with memory; it's a comprehensive cognitive architecture. By deeply integrating a multi-level, actively managed memory system (UMS) with an adaptive, meta-cognitive control loop (AML), it provides a blueprint for AI agents that can reason, plan, learn, and adapt in more complex, robust, and arguably more "intelligent" ways than previously demonstrated. Its focus on integration, self-management, and structured processes positions it as a noteworthy advance in the pursuit of capable and autonomous AI.

Okay, let's walk through how the AgentMasterLoop leveraging the UnifiedMemorySystem (UMS) via the MCPClient might tackle the complex, open-ended task: "Analyze news, journals, and data sources from the past week to identify the most important drivers for US interest rates and US equity indexes." We'll assume the necessary corpus search tools are available.

This process highlights the interplay between structured process management, cognitive memory features, and adaptive meta-cognition.

Phase 1: Initialization and Task Decomposition (Loops 1-3)

1. Loop 1: Workflow Creation

   • AML: Starts, loads state (likely empty or from unrelated prior work). initialize() verifies UMS tools.
   • AML (_gather_context): Context is minimal. Status is "No Active Workflow".
   • AML -> LLM (_call_agent_llm): Prompt contains the goal and minimal context. The system prompt directs it to create a workflow first.
     • LLM Reasoning (Simulated): "Goal is complex analysis. Need a dedicated workflow. Plan: 1. Create workflow 'Interest Rate & Equity Analysis'. 2. Define initial sub-tasks."
     • LLM Output: Tool call create_workflow (title="Interest Rate & Equity Analysis", goal="Identify key drivers..."). Proposes initial plan in reasoning.
   • AML (_execute_tool_call_internal): Executes create_workflow.
   • UMS: Creates the workflow record, assigns an ID (e.g., wf-123), creates a default thought_chain (e.g., tc-abc) linked to it.
   • AML: Updates state: workflow_id = wf-123, context_id = wf-123, workflow_stack = [wf-123], current_thought_chain_id = tc-abc. Sets the initial plan provided by the LLM. last_action_summary updated.

2. Loop 2: Task Structuring (Thought Chains)

   • AML (_gather_context): Context now includes wf-123, goal, tc-abc, and the initial plan.
   • AML -> LLM: Prompt includes the goal and active workflow/thought chain.
     • LLM Reasoning: "Goal has two distinct parts: interest rates and equities. Better to analyze separately first, then synthesize. Create dedicated thought chains for each analysis stream. Plan: 1. Create 'Interest Rate Drivers' thought chain. 2. Create 'Equity Index Drivers' thought chain. 3. Plan initial search for interest rate data."
     • LLM Output: Tool call create_thought_chain (workflow_id=wf-123, title="Interest Rate Drivers"). Updates plan in reasoning.
   • AML: Updates current_plan based on LLM output.
   • AML (_execute_tool_call_internal): Executes create_thought_chain.
   • UMS: Creates the new thought chain record (e.g., tc-rates).
   • AML: Updates state: current_thought_chain_id = tc-rates.

3. Loop 3: Planning Initial Search (Rates)

   • AML (_gather_context): Context reflects wf-123 and the active tc-rates. Plan includes creating the equity chain and then searching.
   • AML -> LLM:
     • LLM Reasoning: "Now focused on 'Interest Rate Drivers' (tc-rates). Plan is to create the equity chain next, but let's first plan the rate search within this chain. Need to identify relevant data types: Fed statements, economic indicators (CPI, jobs), major political/regulatory news, market commentary. Plan: 1. Record thought detailing search strategy for rates in tc-rates. 2. Create 'Equity Index Drivers' thought chain. 3. Execute search for rate drivers."
     • LLM Output: Tool call record_thought (thought_chain_id=tc-rates, thought_type=plan, content="Plan: Search corpus for 'Fed statements last 7 days', 'CPI report analysis', 'Jobs report impact', 'major regulatory news financial sector', 'interest rate market commentary past week'"). Updates plan.
   • AML: Updates plan.
   • AML (_execute_tool_call_internal): Executes record_thought.
   • UMS: Stores the planning thought within tc-rates. Creates linked Episodic memory (memory_type=REASONING_STEP).
   • AML: Counters (successful_actions_since...) increment.

Phase 2: Data Gathering and Initial Analysis (Loops 4-N)

4. Loop 4: Execute Rate Driver Search

   • AML (_gather_context): Context includes the plan to create the equity chain and then search for rates. current_thought_chain_id is still tc-rates.
   • AML -> LLM:
     • LLM Reasoning: "Following plan. First step is creating the equity chain."
     • LLM Output: Tool call create_thought_chain (title="Equity Index Drivers"). Updates plan.
   • AML: Updates plan.
   • AML (_execute_tool_call_internal): Executes create_thought_chain.
   • UMS: Creates tc-equities.
   • AML: State updates current_thought_chain_id = tc-equities. Wait, the LLM plan was search then create chain. Let's assume the LLM was smarter and planned the search first based on the thought recorded in Loop 3.
   • AML -> LLM (Revised Loop 4):
     • LLM Reasoning: "Following plan recorded in tc-rates. Execute search using specified keywords on the pre-indexed corpus."
     • LLM Output: Tool call corpus_search:fused_search (using keywords from the planning thought). Updates plan.
   • AML (_execute_tool_call_internal): Records record_action_start. Executes fused_search. Receives list of document snippets/IDs related to rate drivers. Records record_action_completion storing results snippet/reference.
   • UMS: Stores action logs (Episodic). Maybe an Artifact if results are large.

5. Loop 5: Process Rate Search Results

   • AML (_gather_context): Includes successful search action summary. current_thought_chain_id is tc-rates.
   • AML -> LLM:
     • LLM Reasoning: "Search complete. Need to process results. Many snippets. First, summarize the bulk findings. Then extract key distinct points and store them individually as memories. Plan: 1. Summarize results (Tool: summarize_text). 2. Record summary as an artifact. 3. Store summary memory." Records this plan as a thought in tc-rates.
     • LLM Output: Tool call summarize_text (text_to_summarize=results). Updates plan.
   • AML (_execute_tool_call_internal): Executes summarize_text. Gets summary. Records completion.

6. Loops 6-8: Store Key Rate Findings as Memories

   • AML -> LLM: LLM iterates through summarized/original results.
     • LLM Reasoning: "Finding 1: Fed Chair statement emphasized data dependency. Store as semantic fact, high importance." Records thought in tc-rates.
     • LLM Output: Tool call store_memory (content="Fed Chair emphasized data dependency...", memory_type=fact, importance=8.0, confidence=0.95, source="Corpus Search Results Summary").
   • AML (_execute_tool_call_internal): Executes store_memory.
   • UMS: Stores semantic memory, generates embedding. Background _run_auto_linking triggers, potentially linking this to other Fed-related memories if they exist.
   • ...Repeats for other key findings (CPI data point, jobs report impact, specific regulatory news item)... Each store_memory call increments success counters.

Phase 3: Meta-Cognition and Adaptation (Loop N)

7. Loop N: Reflection Triggered

   • Assume successful_actions_since_reflection reaches current_reflection_threshold (initially BASE_REFLECTION_THRESHOLD).
   • AML (_run_periodic_tasks): Schedules generate_reflection (e.g., type 'gaps').
   • AML (_execute_tool_call_internal): Executes generate_reflection.
   • UMS: Provides recent memory_operations log (creates, accesses, links related to rates).
   • LLM (within generate_reflection): Analyzes operations. Reasoning: "Agent focused heavily on economic data and Fed speak for rates. Limited operations related to political news or broad market sentiment analysis found in logs."
   • UMS: Stores the reflection: "Gap identified: Political stability and broad market sentiment impact on rates not explicitly searched or analyzed yet."
   • AML: last_meta_feedback is set with the reflection summary. successful_actions_since_reflection resets to 0.

8. Loop N+1: Reacting to Reflection & Switching Task

   • AML (_gather_context): Includes the reflection feedback in last_meta_feedback. current_thought_chain_id is tc-rates.
   • AML -> LLM:
     • LLM Reasoning: "Meta-feedback noted lack of political/sentiment analysis for rates. Incorporate later. Current plan requires starting equity analysis. Switch focus. Plan: 1. Record thought acknowledging feedback & switch to Equity Chain (tc-equities). 2. Plan initial equity driver search."
     • LLM Output: Tool call record_thought (thought_chain_id=tc-equities, content="Acknowledging reflection gap on rate drivers (politics/sentiment). Switching focus to equity index analysis."). Updates plan.
   • AML (_execute_tool_call_internal): Executes record_thought.
   • AML: State updates current_thought_chain_id = tc-equities (assuming loop handles switch based on last thought target).

Phase 4: Equity Analysis & Synthesis (Loops N+2 - M)

9. Loops N+2 onwards: Agent repeats the Search -> Analyze -> Store Memory cycle for equities, using tc-equities.

   • It might use hybrid_search_memories on the internal UMS first (query="impact of recent CPI data on equities") before searching the external corpus. This leverages knowledge already stored.
   • It stores key equity drivers (e.g., tech earnings, energy prices, consumer spending indicators) as semantic memories.
   • It potentially uses create_memory_link to explicitly connect a stored rate driver (e.g., "Fed statement") to an equity impact memory (e.g., "Tech stocks declined post-Fed statement") with link_type causal or related.

10. Loop M: Consolidation Triggered

    • Assume successful_actions_since_consolidation hits current_consolidation_threshold OR _adapt_thresholds lowered it due to many new memories.
    • AML (_run_periodic_tasks): Schedules consolidate_memories (type='insight', filter: recent memories from wf-123).
    • AML (_execute_tool_call_internal): Executes consolidate_memories.
    • UMS: Retrieves recent rate & equity driver memories.
    • LLM (within consolidate_memories): Synthesizes findings. Reasoning: "Inflation data (CPI) appears to be the dominant factor influencing both rates (via Fed expectations) and equities (negatively) this week. Tech earnings provided some counter-balance for indexes."
    • UMS: Stores new Insight memory, links it back to source memories.
    • AML: successful_actions_since_consolidation resets.

11. Loop M+1: Adaptation Example

    • AML (_run_periodic_tasks): Schedules compute_memory_statistics. _adapt_thresholds might notice a high ratio of episodic memories still, slightly lowering the consolidation threshold further. It might also see low tool failure rates and slightly increase the reflection threshold.
    • AML: State updates current_consolidation_threshold and current_reflection_threshold.

Phase 5: Final Synthesis and Completion (Loop K)

12. Loop K-1: Planning the Final Report

    • AML (_gather_context): Context includes consolidated insights, summaries for rates/equities.
    • AML -> LLM:
      • LLM Reasoning: "Sufficient analysis performed on both topics. Consolidated insights available. Synthesize findings into final answer. Plan: 1. Generate final report outlining key drivers identified."
      • LLM Output: Updates plan: [{"id": "step_report", "description": "Generate final analysis report", "assigned_tool": "generate_workflow_report", "depends_on": ["action_id_consolidation"]}] (assuming consolidation action ID is known).
    • AML: Updates plan.

13. Loop K: Generating Report & Achieving Goal

    • AML (_execute_tool_call_internal): Reaches step_report. Calls _check_prerequisites (checks if consolidation action is completed). Executes generate_workflow_report.
    • UMS: Provides all necessary data for the report.
    • AML -> LLM:
      • LLM Reasoning: "Report generated. Core task is complete based on analysis and synthesis."
      • LLM Output: "Goal Achieved: Key drivers for US rates this week were primarily CPI data exceeding expectations and subsequent Fed commentary reinforcing data dependency. Key equity index drivers included negative reactions to rate fears, partially offset by strong tech sector earnings reports. Political and regulatory news played a secondary role."
    • AML: Detects "Goal Achieved:", sets goal_achieved_flag=True.
    • AML (_execute_tool_call_internal): Calls update_workflow_status to mark wf-123 as completed.

Phase 6: Shutdown

14. AML: Loop terminates. Calls _generate_final_report (displaying the previously generated one or generating again). Saves final state. Cleans up background tasks.

This walkthrough illustrates how the AML orchestrates the LLM and UMS tools. The key is the constant flow of information from the UMS (context, memory search, stats) to the LLM, and back into the UMS (actions, thoughts, new memories, links, reflections) under the guidance of the adaptive AML cycle. Features like thought chains, dependency checking, and meta-cognition allow it to handle the complexity and ambiguity inherent in such an open-ended task.

Okay, let's simulate the financial analysis task with extreme granularity, focusing on the detailed interactions between the Agent Master Loop (AML), the Unified Memory System (UMS) tools, and the underlying database state.

Task: Analyze news, journals, and data sources from the past week to identify the most important drivers for US interest rates and US equity indexes.

Initial State:

• AgentState: Default values (loop 0, empty plan, no workflow ID, etc.)
• UMS DB: Empty (financial_analysis.db)
• Thresholds: Reflection=7, Consolidation=12

Loop 1: Initialization & Workflow Creation

1. AML run starts: state = AgentState(). current_loop = 0. Calls initialize().
2. AML initialize: Loads empty state. Connects to financial_analysis.db (implicitly creating it). Verifies UMS tools (create_workflow, record_thought, etc.) are available via MCPClient._find_tool_server. Assume OK. state.workflow_id is None. Returns True.
3. AML run: state.current_loop = 1. Calls _gather_context().
4. AML _gather_context: No active workflow_id. Returns {"status": "No Active Workflow", ...}.
5. AML run: Recognizes "No Active Workflow". Sets state.current_plan = [PlanStep(id='step-1a', description="Create workflow for 'Interest Rate & Equity Analysis'")]. state.needs_replan = False.
6. AML run: Calls _call_agent_llm(goal, context={"status": "No Active Workflow", ...}).
7. AML _call_agent_llm: Constructs prompt: System prompt details UMS tools. User prompt contains the goal, the "No Active Workflow" context, and the instruction to create a workflow first. Sends to LLM.
8. LLM Response:
   • Text (Simulated): "Understood. The first step is to establish a workflow for this complex financial analysis task. Reasoning: Essential for tracking progress, actions, and findings. Updated Plan:\njson\n[{\"id\": \"step-1b\", \"description\": \"Structure analysis: Create dedicated thought chains for Rates and Equities\", \"status\": \"planned\", \"depends_on\": [\"act-create-wf\"]}]\n" (LLM anticipates the action ID for the workflow creation)
   • Tool Call: {"name": "create_workflow", "input": {"title": "Interest Rate & Equity Analysis - Week [Current Date]", "goal": "Identify key drivers for US interest rates and equity indexes over the past week.", "tags": ["financial_analysis", "rates", "equities", "weekly"]}}
9. AML _call_agent_llm: Parses response. Extracts tool call create_workflow and updated_plan_steps. Returns decision dict.
10. AML run: agent_decision received. Checks updated_plan_steps: Valid. Updates state.current_plan = [PlanStep(id='step-1b', ...)]. state.needs_replan = False.
11. AML run: decision_type is call_tool. Calls _execute_tool_call_internal(tool_name="create_workflow", arguments={...}, record_action=True, planned_dependencies=None).
12. AML _execute_tool_call_internal:
    • Finds server for create_workflow.
    • Checks dependencies: None.
    • Determines should_record_start = False for create_workflow. Skips _record_action_start_internal.
    • Executes create_workflow tool via MCPClient.execute_tool.
    • UMS Tool create_workflow:
      • Generates workflow_id="wf-fin1", thought_chain_id="tc-main-fin1".
      • now_iso = datetime.utcnow().isoformat(). now_unix = int(time.time()).
      • DB Interaction:
        • INSERT INTO workflows (workflow_id, title, goal, status, created_at, updated_at, last_active, tags_list) VALUES ('wf-fin1', 'Interest Rate & Equity Analysis...', 'Identify key drivers...', 'active', now_iso, now_iso, now_unix, ['financial_analysis', ...])
        • Calls MemoryUtils.process_tags -> INSERT INTO tags ... ON CONFLICT DO NOTHING, SELECT tag_id, INSERT OR IGNORE INTO workflow_tags.
        • INSERT INTO thought_chains (thought_chain_id, workflow_id, title, created_at) VALUES ('tc-main-fin1', 'wf-fin1', 'Main reasoning for: Interest Rate & Equity Analysis...', now_iso)
        • COMMIT.
      • Returns {"success": True, "workflow_id": "wf-fin1", "primary_thought_chain_id": "tc-main-fin1", "created_at": now_iso}. Assume hypothetical Action ID assigned by framework/logging is act-create-wf.
    • tool_result_content received (success=True). state.last_action_summary = "Tool create_workflow executed. Success: True...". state.last_error_details = None. state.consecutive_error_count = 0.
    • Calls _handle_workflow_side_effects.
    • AML _handle_workflow_side_effects: Matches create_workflow. Updates state.workflow_id="wf-fin1", state.context_id="wf-fin1", state.workflow_stack=["wf-fin1"], state.current_thought_chain_id="tc-main-fin1". Logs state changes. Plan remains as set from LLM (step-1b).
    • Returns tool_result_content.
13. AML run: Skips heuristic _update_plan.
14. AML run: Calls _run_periodic_tasks. Counters successful_actions_since... are 0. Nothing happens.
15. AML run: state.current_loop = 1 ends. Saves state (wf-fin1, tc-main-fin1, plan step-1b, etc.). Sleeps.

Loop 2: Planning Thought Chain Creation

1. AML run: state.current_loop = 2. Calls _gather_context().
2. AML _gather_context: Fetches core context for wf-fin1 (active). Working memory empty. Proactive search for "Structure analysis..." likely finds little internal memory. Returns context including workflow_id, current_thought_chain_id="tc-main-fin1", and plan step-1b.
3. AML run: Current plan step step-1b: "Structure analysis...". Dependency act-create-wf.
4. AML run: Calls _call_agent_llm.
5. LLM Response:
   • Text: "Okay, structuring the analysis. The goal has two main components: interest rates and equity indexes. It's best to handle these in separate reasoning streams (thought chains) for clarity and potential parallel processing later if needed. First, I'll plan to create the chain for interest rates. Updated Plan:\njson\n[{\"id\": \"step-2a\", \"description\": \"Create 'Interest Rate Drivers' thought chain\", \"status\": \"planned\", \"depends_on\": [\"act-create-wf\"]}, {\"id\": \"step-2b\", \"description\": \"Create 'Equity Index Drivers' thought chain\", \"status\": \"planned\", \"depends_on\": [\"act-create-rates-chain\"]}, {\"id\": \"step-2c\", \"description\": \"Plan initial search strategy for Interest Rates\", \"status\": \"planned\", \"depends_on\": [\"act-create-equity-chain\"]}]\n" (LLM breaks down the structuring task)
   • Tool Call: {"name": "create_thought_chain", "input": {"workflow_id": "wf-fin1", "title": "Interest Rate Drivers"}}
6. AML _call_agent_llm: Parses. Returns decision.
7. AML run: Updates state.current_plan with the new 3-step plan.
8. AML run: decision_type is call_tool (create_thought_chain). Plan step is now step-2a. Checks dependency act-create-wf. Calls _check_prerequisites(["act-create-wf"]).
9. AML _check_prerequisites: Calls _execute_tool_call_internal(get_action_details, {"action_ids": ["act-create-wf"]}). (Simulated: UMS returns status completed for act-create-wf). Returns (True, "All dependencies completed").
10. AML run: Prerequisites met. Continues with _execute_tool_call_internal for create_thought_chain.
11. AML _execute_tool_call_internal:
    • Finds server. Skips recording start/completion.
    • Injects workflow_id="wf-fin1". Executes tool.
    • UMS Tool create_thought_chain:
      • Generates thought_chain_id="tc-rates1".
      • DB Interaction: INSERT INTO thought_chains ... VALUES ('tc-rates1', 'wf-fin1', 'Interest Rate Drivers', now_iso). UPDATE workflows SET updated_at=now_iso, last_active=now_unix WHERE workflow_id='wf-fin1'. COMMIT.
      • Returns {"success": True, "thought_chain_id": "tc-rates1", ...}. Action ID act-create-rates-chain.
    • success=True. last_action_summary updated.
    • Calls _handle_workflow_side_effects.
    • AML _handle_workflow_side_effects: Updates state.current_thought_chain_id="tc-rates1". Logs switch.
    • Returns result.
12. AML run: Calls heuristic _update_plan. Marks step-2a completed. Plan [step-2b, step-2c].
13. AML run: Periodic tasks. Nothing significant. Loop ends.

Loop 3: Create Equity Thought Chain

1. AML run: state.current_loop = 3. Context includes current_thought_chain_id="tc-rates1". Plan step step-2b: "Create Equity Chain". Dependency act-create-rates-chain.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Creating the second thought chain for equity index drivers as planned." Updated Plan: [Marks step-2b in progress]
   • Tool Call: {"name": "create_thought_chain", "input": {"workflow_id": "wf-fin1", "title": "Equity Index Drivers"}}
4. AML: Updates plan status. Executes create_thought_chain. Checks dependency act-create-rates-chain (completed).
5. AML _execute_tool_call_internal: Executes tool. Action ID act-create-equity-chain.
6. UMS: Creates tc-equities1.
7. AML _handle_workflow_side_effects: Updates state.current_thought_chain_id="tc-equities1". Logs switch.
8. AML: Heuristic updates plan (removes step-2b). Plan [step-2c].

Loop 4: Plan Rate Search Strategy (Thought)

1. AML run: state.current_loop = 4. current_thought_chain_id="tc-equities1". Plan step step-2c: "Plan initial search strategy for Interest Rates". Dependency act-create-equity-chain.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Now planning the interest rate search. Need to ensure this plan is recorded in the correct thought chain (tc-rates1). Relevant data types: Fed statements, economic reports (CPI, jobs this week), key regulatory news, market commentary. Updated Plan:\njson\n[{\"id\": \"step-4a\", \"description\": \"Record Rate Search Strategy Thought (in tc-rates1)\", \"status\": \"planned\", \"depends_on\": [\"act-create-equity-chain\"]}, {\"id\": \"step-4b\", \"description\": \"Execute search for rate drivers\", \"status\": \"planned\", \"depends_on\": [\"act-rec-rate-strat\"]}]\n" (LLM corrects sequencing and explicitly targets the rate chain)
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-fin1", "thought_chain_id": "tc-rates1", "thought_type": "plan", "content": "Rate Driver Search Plan: Query corpus using fused search for terms: 'Federal Reserve statement last 7 days', 'FOMC minutes impact', 'CPI report analysis [Current Month/Year]', 'Nonfarm payrolls jobs report analysis [Current Month/Year]', 'Treasury auction results analysis week ending [Date]', 'interest rate market commentary past week', 'financial regulation news past week'."}} (Specifies target chain)
4. AML: Updates plan (replaces step-2c). Executes record_thought. Checks dependency act-create-equity-chain (completed).
5. AML _execute_tool_call_internal:
   • Skips recording action start/complete.
   • Injects workflow_id. Uses thought_chain_id="tc-rates1" from arguments. Executes tool. Action ID act-rec-rate-strat.
   • UMS Tool record_thought:
     • Generates thought_id="th-rate-plan1". seq_no = 1 (for tc-rates1).
     • DB Interaction: INSERT INTO thoughts (thought_id, thought_chain_id, thought_type, content, sequence_number, created_at) VALUES ('th-rate-plan1', 'tc-rates1', 'plan', 'Rate Driver Search Plan...', 1, now_iso).
     • Generates memory_id="mem-rate-plan1".
     • DB Interaction: INSERT INTO memories (memory_id, workflow_id, thought_id, content, memory_level, memory_type, ...) VALUES ('mem-rate-plan1', 'wf-fin1', 'th-rate-plan1', 'Thought [1] (Plan): Rate Driver Search Plan...', 'episodic', 'reasoning_step', ...)
     • COMMIT.
     • Returns {"success": True, "thought_id": "th-rate-plan1", "linked_memory_id": "mem-rate-plan1"}.
   • success=True. last_action_summary updated.
   • Triggers background _run_auto_linking for mem-rate-plan1.
   • Returns result.
6. AML: Heuristic updates plan (removes step-4a). Plan [step-4b]. Important: state.current_thought_chain_id remains "tc-equities1" because the last explicit switch was to that chain.
7. AML run: Periodic tasks. Nothing significant. Loop ends.

Loop 5: Execute Rate Driver Search

1. AML run: state.current_loop = 5. current_thought_chain_id="tc-equities1". Plan step step-4b: "Execute search for rate drivers". Dependency act-rec-rate-strat.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Executing the planned search for interest rate drivers using the strategy from thought th-rate-plan1. The search terms target Fed comms, CPI, jobs, and commentary. Updated Plan:\njson\n[{\"id\": \"step-5a\", \"description\": \"Summarize rate search results\", \"status\": \"planned\", \"depends_on\": [\"act-exec-rate-search\"]}]\n"
   • Tool Call: {"name": "corpus_search:fused_search", "input": {"query": "Federal Reserve statement last 7 days OR FOMC minutes impact OR CPI report analysis [Current Month/Year] OR ...", "k": 50}} (Constructs query from th-rate-plan1)
4. AML: Updates plan (replaces step-4b). Executes fused_search. Checks dependency act-rec-rate-strat (completed).
5. AML _execute_tool_call_internal:
   • Determines should_record_start = True. Calls _record_action_start_internal.
   • AML _record_action_start_internal: Executes record_action_start (workflow_id=wf-fin1, type=tool_use, tool_name=corpus_search:fused_search, ...). Returns action_id="act-exec-rate-search".
   • Executes corpus_search:fused_search tool via MCP.
   • (Simulated Tool Execution): Tool queries external index, returns {"results": [{"id": "doc1", "snippet": "Fed Chair Powell reiterated data dependency..."}, {"id": "doc2", "snippet": "CPI index rose 0.X% MoM..."}, ...], "count": 45}.
   • tool_result_content received. success=True.
   • Calls _record_action_completion_internal.
   • AML _record_action_completion_internal: Executes record_action_completion (action_id=act-exec-rate-search, status=completed, tool_result={preview}).
   • UMS: actions table updated for act-exec-rate-search (status, completed_at, tool_result). Episodic memory for action completion updated.
   • success=True. last_action_summary updated. state.successful_actions_since... counters incremented to 1.
   • Returns result.
6. AML: Heuristic updates plan (removes step that led to search). Plan [step-5a].
7. AML run: Loop ends.

Loop 6: Summarize Rate Results (and Store Summary)

1. AML run: state.current_loop = 6. Plan step-5a: "Summarize rate results". Dependency act-exec-rate-search. current_thought_chain_id="tc-equities1".
2. AML run: Calls _call_agent_llm. Context includes summary of act-exec-rate-search results.
3. LLM Response:
   • Text: "The search yielded 45 relevant snippets. Summarizing these to extract key themes regarding rate drivers. Will store the summary as a distinct semantic memory. Updated Plan:\njson\n[{\"id\": \"step-6a\", \"description\": \"Record thought: Switch back to tc-rates to analyze summary\", \"status\": \"planned\", \"depends_on\": [\"act-summarize-rates\"]}]\n"
   • Tool Call: {"name": "summarize_text", "input": {"text_to_summarize": "[Combined text of 45 snippets...]", "target_tokens": 750, "workflow_id": "wf-fin1", "record_summary": true}} (Instructs tool to store)
4. AML: Updates plan. Executes summarize_text. Checks dependency act-exec-rate-search (completed).
5. AML _execute_tool_call_internal:
   • Records start (act-summarize-rates). Executes tool.
   • UMS Tool summarize_text:
     • Calls LLM (internal or via gateway) to summarize input text.
     • Receives summary: "Key themes: Higher-than-expected CPI print caused market reaction. Fed officials (Powell, Williams) emphasized data dependency, downplayed immediate cuts. Jobs report strong but wage growth moderated slightly..."
     • Since record_summary=True: Generates memory_id="mem-rates-summary1".
     • DB Interaction: INSERT INTO memories (memory_id, workflow_id, content, memory_level, memory_type, importance, confidence, description, source, ...) VALUES ('mem-rates-summary1', 'wf-fin1', '[Summary Text]', 'semantic', 'summary', 7.0, 0.9, 'Summary of rate drivers search results...', 'summarize_text', ...)
     • Calls _log_memory_operation for the summary creation.
     • COMMIT.
     • Returns {"success": True, "summary": "[Summary Text]", "stored_memory_id": "mem-rates-summary1"}.
   • Records completion for act-summarize-rates.
   • success=True. last_action_summary updated. Counters increment to 2.
   • Triggers background _run_auto_linking for mem-rates-summary1.
   • Returns result.
6. AML: Heuristic updates plan (removes step-5a). Plan [step-6a].
7. AML run: Loop ends.

Loop 7: Explicit Thought Chain Switch

1. AML run: state.current_loop = 7. Plan step-6a: "Record thought: Switch back...". Dependency act-summarize-rates. current_thought_chain_id="tc-equities1".
2. AML run: Calls _call_agent_llm. Context includes mem-rates-summary1.
3. LLM Response:
   • Text: "Summary memory mem-rates-summary1 created. Now switching thought context back to tc-rates1 to perform detailed analysis of that summary. Updated Plan:\njson\n[{\"id\": \"step-7a\", \"description\": \"Analyze rate summary (mem-rates-summary1) and store key facts\", \"status\": \"planned\", \"depends_on\": [\"act-rec-switch-rates\"]}]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-fin1", "thought_chain_id": "tc-rates1", "content": "Switching focus back to interest rate analysis chain (tc-rates1). Will now analyze summary memory mem-rates-summary1.", "thought_type": "reasoning"}}
4. AML: Updates plan. Executes record_thought. Checks dependency.
5. AML _execute_tool_call_internal: Executes tool. Action ID act-rec-switch-rates.
6. UMS: Stores thought th-switch-rates1 in tc-rates1. Creates linked memory mem-switch-rates1.
7. AML: Background linking. Heuristic updates plan (removes step-6a). Plan [step-7a]. AML detects thought recorded in tc-rates1, updates state.current_thought_chain_id = "tc-rates1".
8. AML run: Loop ends.

Loops 8-10: Storing Key Rate Facts from Summary

1. AML run: state.current_loop = 8. Plan step-7a: "Analyze rate summary...". Dependency act-rec-switch-rates. current_thought_chain_id="tc-rates1".
2. AML run: Calls _call_agent_llm. Context includes mem-rates-summary1.
3. LLM Response:
   • Text: "Analyzing mem-rates-summary1. First key fact: CPI print details. Storing as distinct semantic memory. Updated Plan:\njson\n[{\"id\": \"step-7a\", \"description\": \"Analyze rate summary...\", \"status\": \"in_progress\"}, {\"id\": \"step-8a\", \"description\": \"Store Fact: Hot CPI Print\", \"status\": \"planned\", \"depends_on\": [\"act-rec-switch-rates\"]}, {\"id\": \"step-8b\", \"description\": \"Store Fact: Fed Reaction Quote\", ...}]\n"
   • Tool Call: {"name": "store_memory", "input": {"workflow_id": "wf-fin1", "memory_level": "semantic", "memory_type": "fact", "content": "CPI data for [Month, Year] released [Date] showed X.X% YoY increase, above consensus of Y.Y%. Core CPI was Z.Z%.", "description": "Fact: Higher-than-expected CPI Print ([Month, Year])", "source": "mem-rates-summary1", "importance": 9.0, "confidence": 0.98, "tags": ["cpi", "inflation", "economic_data", "[Month]", "[Year]"]}}
4. AML: Updates plan. Executes store_memory. Checks dependency.
5. AML _execute_tool_call_internal: Records start/completion (act-store-cpi-fact).
6. UMS: Stores mem-cpi-fact1. Embeds. Background linking might connect to prior CPI memories.
7. AML: Plan status updated by heuristic. Counters increment to 3.
8. Loop 9 (Store Fed Reaction): LLM calls store_memory for Fed quote (mem-fed-react1). Updates plan. act-store-fed-react depends on act-store-cpi-fact. Counter -> 4.
9. Loop 10 (Store Jobs Data): LLM calls store_memory for Jobs number (mem-jobs-fact1). Updates plan. act-store-jobs-fact depends on act-store-fed-react. Counter -> 5. LLM marks step-7a completed in next proposed plan.

Loop 11: Reflection Trigger

1. AML run: state.current_loop = 11. Assume state.successful_actions_since_reflection = 5 (Store CPI, Fed, Jobs + Summary + Search = 5) < 7. No reflection yet. Plan likely involves switching to equity analysis now.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Key rate facts stored. Completed initial rate driver analysis phase. Switching focus to equity index analysis in chain tc-equities1. Updated Plan:\njson\n[{\"id\": \"step-11a\", \"description\": \"Record thought: Switch to Equities\", \"status\": \"planned\", \"depends_on\": [\"act-store-jobs-fact\"]}, ...]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-fin1", "thought_chain_id": "tc-equities1", "content": "Switching focus to equity index analysis (tc-equities1). Completed rate fact extraction.", "thought_type": "reasoning"}}
4. AML: Updates plan. Executes record_thought. Checks dependency.
5. AML _execute_tool_call_internal: Executes record_thought (Action act-switch-eq).
6. UMS: Stores th-switch-eq1 in tc-equities1. Creates mem-switch-eq1.
7. AML: Heuristic updates plan. Updates state.current_thought_chain_id = "tc-equities1".

Loops 12-19 (Equity Analysis Phase):

• Loop 12: LLM plans equity search (e.g., earnings, sector news, sentiment) in tc-equities1. Records thought th-eq-plan1. Counter -> 6.
• Loop 13 (Potential Reflection): state.successful_actions_since_reflection = 6. Still < 7. No reflection.
• Loop 13 (cont.): LLM executes corpus_search:fused_search for equity terms (act-exec-eq-search). Counter -> 7.
• Loop 14 (Reflection Trigger): state.successful_actions_since_reflection = 7. Reaches threshold.
• AML run: Calls _run_periodic_tasks. Schedules generate_reflection (type='gaps', cycle index 1). Executes it.
  • UMS: Provides logs (rate facts stored, switch thought, equity search action).
  • LLM (in reflection): "Gap Analysis: Rate analysis focused on data/Fed. Equity search initiated. Missing explicit analysis of interaction/correlation between rates and equities. Also missing political/geopolitical factor search for both."
  • UMS: Stores reflection ref-gaps1.
  • AML: last_meta_feedback set. successful_actions_since_reflection = 0. needs_replan = True.
• Loop 14 (cont.): Calls _call_agent_llm. Context includes feedback and needs_replan=True. Equity search results also available from act-exec-eq-search.
• LLM Response:
  • Text: "Reflection noted gaps (rate/equity interaction, politics). Will incorporate after processing current equity search results. Processing equity results now. Plan: 1. Summarize equity results. 2. Store key equity facts. 3. Plan search for interaction/political factors. Updated Plan:\njson\n[{\"id\": \"step-14a\", \"description\": \"Summarize equity search results\", \"status\": \"planned\", \"depends_on\": [\"act-exec-eq-search\"]}, ...]\n" (LLM acknowledges feedback, defers acting on it, focuses on current results, resets replan flag implicitly)
  • Tool Call: {"name": "summarize_text", "input": {"text_to_summarize": "[Equity Snippets]", "record_summary": true}}
• AML: Updates plan. needs_replan = False. Executes summarize_text (act-summarize-eq). Stores mem-eq-summary1. Counter -> 1.
• Loops 15-17: LLM analyzes mem-eq-summary1, calls store_memory for key equity facts (e.g., mem-tech-earn1, mem-oil-impact1) in tc-equities1. Counters -> 2, 3, 4.
• Loop 18 (Linking): LLM explicitly links CPI fact to equity summary: create_memory_link(source=mem-cpi-fact1, target=mem-eq-summary1, type=causal). Action act-link-cpi-mkt. Counter -> 5.
• Loop 19 (Planning Next Search): LLM acts on earlier reflection feedback.
  • LLM Reasoning: "Equity facts stored. Now address reflection gap: search for rate/equity interaction analysis and political factors."
  • LLM Output: Records thought (th-plan-gap-search) in tc-equities1 (or maybe tc-main-fin1 for synthesis?). Plan includes executing this new search. Counter -> 6.

Loops 20-22 (Gap Filling & Consolidation):

• Loop 20: Executes search based on th-plan-gap-search (act-exec-gap-search). Counter -> 7.
• Loop 21 (Reflection Trigger): successful_actions_since_reflection = 7. Reflection runs (type='progress'). Feedback might be "Good progress filling gaps". successful_actions_since_reflection = 0.
• Loop 21 (cont.): LLM summarizes gap search results (act-summarize-gap). Stores mem-gap-summary1. Counter -> 1.
• Loop 22 (Consolidation Trigger): Assume successful_actions_since_consolidation (tracking store_memory, maybe link actions) hits threshold (e.g., 12). _run_periodic_tasks calls consolidate_memories (type=insight, filter: recent wf-fin1 memories).
  • LLM (in consolidation): Synthesizes mem-rates-summary1, mem-eq-summary1, mem-gap-summary1, key facts. Generates core insight about CPI dominance, tech resilience, secondary political role.
  • UMS: Stores mem-insight1. Links back to sources.
  • AML: last_meta_feedback set. successful_actions_since_consolidation = 0. needs_replan = True.

Loops 23-25 (Final Reporting & Completion):

• Loop 23: LLM reacts to consolidation insight (last_meta_feedback). Plans final report using generate_workflow_report. Updates plan [step-report (depends on act-consolidate1)]. needs_replan = False.
• Loop 24: Executes generate_workflow_report. Checks dependency. UMS provides data. Report generated. Action act-gen-report. Counter -> 2.
• Loop 25: LLM sees report generated.
  • LLM Output: "Goal Achieved: [Final summary text based on consolidation/report]..."
  • AML: Detects "Goal Achieved:", sets flag. Calls update_workflow_status -> completed.

Loop 26: Shutdown

• AML terminates loop. Generates/displays final report. Saves state. Cleans up.

This incredibly detailed flow emphasizes the constant state checking, dependency validation, context switching between thought chains, explicit memory operations, and the interleaving of task execution with adaptive meta-cognitive processes that characterize this advanced architecture.

Okay, let's walk through how the AgentMasterLoop + UnifiedMemorySystem might tackle the creative task: "Develop a creative, novel TV series concept with broad appeal, create character profiles, a first-season story arc, and write the pilot episode script."

This scenario emphasizes memory synthesis, structured creative planning, artifact generation, and potentially using reflection for creative refinement. We'll assume tools for searching internal memory (including semantic/hybrid search) and potentially a (simulated) external tool for "checking concept novelty" against existing shows are available.

Phase 1: Initialization and Concept Generation (Loops 1-5)

1. Loop 1: Workflow & Initial Brainstorming Plan

   • AML: Initializes, loads state.
   • AML (_gather_context): Minimal context. "No Active Workflow".
   • AML -> LLM: Receives the goal.
     • LLM Reasoning: "Goal requires creative ideation, character dev, story arc, and scriptwriting. Need a workflow. Initial plan: Brainstorm genres and potential concept spaces."
     • LLM Output: create_workflow (title="Novel TV Concept Gen", goal="Develop original series concept, characters, arc, pilot script"). Updates plan: [{"description": "Brainstorm potential genres and high-level concepts"}]
   • AML: Creates workflow (wf-tv123), default thought chain (tc-main). Updates state (workflow_id, context_id, current_thought_chain_id = tc-main). Sets plan.

2. Loop 2: Genre/Concept Brainstorming (Internal Search & Thought)

   • AML (_gather_context): Context includes workflow, goal, plan.
   • AML -> LLM:
     • LLM Reasoning: "Executing brainstorm step. What genres are popular but maybe underserved with novel twists? Sci-fi, Fantasy, Mystery, Historical... Let's search internal memory for 'successful TV show tropes' and 'underused genre combinations'. Then record brainstorming thoughts." Updates plan: [{"description": "Search memory for tropes/combos"}, {"description": "Record brainstormed ideas"}]
     • LLM Output: hybrid_search_memories (query="successful TV show tropes OR underused genre combinations", limit=10, include_content=False).
   • AML (_execute_tool_call_internal): Executes search.
   • UMS: Returns memories related to common tropes, successful shows, maybe past agent reflections on media (if any exist).
   • AML: Updates state (last_action_summary includes search results preview).

3. Loop 3: Recording Brainstormed Ideas

   • AML (_gather_context): Context includes memory search results. Plan step is "Record brainstormed ideas".
   • AML -> LLM:
     • LLM Reasoning: "Memory results show prevalence of 'chosen one' in fantasy, 'gritty detective' in mystery. Underused combo idea: What about a 'slice-of-life comedy set in a high-stakes bureaucratic afterlife'? Or 'historical drama focused on the assistants to famous figures'? Or 'sci-fi mystery where memories are physical objects that can be stolen/traded'? Let's record these." Updates plan: [{"description": "Select most promising concept"}, ...]
     • LLM Output: record_thought (thought_type=hypothesis, content="Idea 1: Bureaucratic Afterlife Comedy.").
   • AML (_execute_tool_call_internal): Executes record_thought.
   • UMS: Stores thought in tc-main. Creates linked episodic memory.
   • (AML likely loops 2 more times to record the other two ideas as thoughts.)

4. Loop 4: Selecting Promising Concept (Thought & Memory)

   • AML (_gather_context): Context includes the three brainstormed ideas as thoughts. Plan step is "Select most promising concept".
   • AML -> LLM:
     • LLM Reasoning: "Evaluating ideas: Afterlife Comedy - high potential for humor/satire, broad appeal possible. Historical Assistants - niche, potentially less broad appeal. Memory Objects Sci-Fi - intriguing, complex, maybe harder execution. Let's pursue the 'Bureaucratic Afterlife Comedy'. Need to store this decision and the core concept." Updates plan: [{"description": "Check concept novelty"}, {"description": "Develop core premise"}, ...]
     • LLM Output: record_thought (thought_type=decision, content="Selected concept: Bureaucratic Afterlife Comedy.").
   • AML (_execute_tool_call_internal): Executes record_thought.
   • UMS: Stores decision thought.
   • AML -> LLM (Implicit next step or combined):
     • LLM Reasoning: "Store the core concept as a semantic memory for future reference."
     • LLM Output: store_memory (memory_type=fact, memory_level=semantic, content="TV Concept: Slice-of-life comedy set in a mundane, bureaucratic afterlife where souls navigate paperwork, office politics, and existential dread with humor.", importance=8.0, confidence=0.7).
   • AML (_execute_tool_call_internal): Executes store_memory.
   • UMS: Stores semantic memory, generates embedding. Background linking might connect it to memories about comedy or bureaucracy.

5. Loop 5: Checking Concept Novelty (Simulated External Tool)

   • AML (_gather_context): Context includes selected concept memory. Plan step is "Check concept novelty".
   • AML -> LLM:
     • LLM Reasoning: "Need to verify if 'Bureaucratic Afterlife Comedy' is truly novel. Use the external tool to search known TV shows/concepts."
     • LLM Output: external_tools:check_concept_novelty (query="TV shows bureaucratic afterlife comedy"). Updates plan.
   • AML (_execute_tool_call_internal): Executes simulated tool call. Assume tool returns "Low overlap found. Similar themes in 'The Good Place' (philosophy/ethics focus) and 'Miracle Workers' (heavenly workplace), but pure bureaucratic slice-of-life angle appears relatively unique."
   • AML (_execute_tool_call_internal): Records action completion with the tool's result.
   • AML -> LLM (Implicit): Records thought: "Novelty check confirms concept is viable with differentiation."

Phase 2: Developing the Premise, Characters, and Arc (Loops 6-15)

6. Loop 6: Defining Core Premise (Memory & Thought Chain)

   • AML (_gather_context): Concept validated. Plan step: "Develop core premise". current_thought_chain_id is tc-main.
   • AML -> LLM:
     • LLM Reasoning: "Flesh out the core premise. Setting: 'The Intermediate Plane - Department of Soul Processing (DSP)'. Tone: Mix of 'The Office'/'Parks & Rec' mundane humor with existential themes. Main conflict source: Incompetent management, arcane rules, inter-departmental rivalries, occasional soul glitches. Let's create a dedicated chain for this concept's development." Updates plan: [{"description": "Create 'Afterlife Comedy Dev' thought chain"}, {"description": "Record detailed premise in new chain"}, ...]
     • LLM Output: create_thought_chain (title="Afterlife Comedy Dev").
   • AML: Creates workflow (wf-tv123). Executes create_thought_chain.
   • UMS: Creates new thought chain (tc-dev).
   • AML: Updates state: current_thought_chain_id = tc-dev.

7. Loop 7: Record Detailed Premise

   • AML (_gather_context): Now in tc-dev. Plan: "Record detailed premise".
   • AML -> LLM:
     • LLM Reasoning: "Record the premise details developed last loop within the new tc-dev chain."
     • LLM Output: record_thought (thought_chain_id=tc-dev, thought_type=summary, content="Premise: Comedy set in the DSP... [full details]").
   • AML (_execute_tool_call_internal): Executes record_thought.
   • UMS: Stores detailed premise thought in tc-dev.

8. Loops 8-10: Character Profile Generation (Thought -> Store Memory -> Link)

   • AML -> LLM: Focuses on creating the main character (e.g., a newly deceased, bewildered soul).
     • LLM Reasoning: "Develop protagonist: Name: Alex Chen. Recently deceased accountant. Personality: Organized, anxious, overwhelmed by afterlife chaos. Goal: Navigate bureaucracy, find meaning (or just get decent coffee). Record profile." Updates plan: [{"description": "Store Alex Chen profile memory"}, ...].
     • LLM Output: record_thought (thought_chain_id=tc-dev, thought_type=hypothesis, content="Character Profile: Alex Chen - Accountant, anxious...").
   • AML (_execute_tool_call_internal): Records thought.
   • AML -> LLM (Next Step):
     • LLM Reasoning: "Store Alex's profile as structured memory."
     • LLM Output: store_memory (memory_type=character_profile, memory_level=semantic, content=json.dumps({"name": "Alex Chen", "role": "Protagonist", ...}), description="Profile for Alex Chen", importance=7.0).
   • AML (_execute_tool_call_internal): Stores memory (mem-alex).
   • (Repeats for other key characters: cynical angel supervisor, overly enthusiastic demon intern, ancient entity running the archives... Each gets a thought, a stored memory, and potentially links related back to the main concept memory.)

9. Loops 11-13: Season 1 Story Arc Development

   • AML -> LLM: Focuses on the overall narrative for the first season.
     • LLM Reasoning: "Develop Season 1 arc: Alex arrives, struggles with rules, forms unlikely alliances (angel/demon), uncovers minor bureaucratic conspiracy/inefficiency, achieves small win/acceptance by finale. Theme: Finding humanity in the mundane, even after death. Record arc points." Updates plan: [{"description": "Store Season 1 Arc memory"}, ...].
     • LLM Output: record_thought (thought_chain_id=tc-dev, thought_type=plan, content="S1 Arc: Arrival -> Struggle -> Alliance -> Minor Conspiracy -> Resolution...").
   • AML (_execute_tool_call_internal): Records thought.
   • AML -> LLM (Next Step):
     • LLM Reasoning: "Store Season 1 Arc as structured memory."
     • LLM Output: store_memory (memory_type=story_arc, memory_level=semantic, content="Season 1: [Detailed arc points]", description="Season 1 Story Arc for Afterlife Comedy", importance=7.5).
   • AML (_execute_tool_call_internal): Stores memory (mem-arc). Links it (related) to the main concept memory.

10. Loop 14: Reflection on Development

    • AML (_run_periodic_tasks): Assume reflection threshold is met. Schedules generate_reflection (type 'strengths').
    • AML (_execute_tool_call_internal): Executes reflection.
    • UMS: Provides logs showing chain creation, character/arc storage.
    • LLM (within reflection): Reasoning: "Agent successfully structured creative process using dedicated thought chain. Character concepts seem distinct. Arc has potential for episodic structure and character growth."
    • UMS: Stores reflection.
    • AML: Gets positive feedback in last_meta_feedback.

Phase 3: Pilot Script Generation (Loops 15-M)

11. Loop 15: Planning Pilot Episode

    • AML (_gather_context): Context includes positive feedback, core concept, characters, S1 arc. Current chain tc-dev. Plan step: "Generate Pilot Script".
    • AML -> LLM:
      • LLM Reasoning: "Ready to write pilot. Pilot should introduce Alex, the setting (DSP), key supporting characters (supervisor, intern), establish tone, and set up initial conflict (e.g., lost paperwork, impossible task). Let's create a dedicated chain for the script." Updates plan: [{"description": "Create 'Pilot Script Gen' thought chain"}, {"description": "Outline Pilot Scenes"}, ...].
      • LLM Output: create_thought_chain (title="Pilot Script Gen").
    • AML: Creates workflow (wf-tv123). Executes create_thought_chain.
    • UMS: Creates tc-pilot.
    • AML: Updates state: current_thought_chain_id = tc-pilot.

12. Loops 16-18: Outlining Pilot Scenes

    • AML -> LLM: Iteratively outlines scenes.
      • LLM Reasoning: "Scene 1: Alex arrives disoriented in sterile waiting room. Scene 2: Meets cynical angel supervisor, gets overwhelming stack of forms. Scene 3: Encounters enthusiastic demon intern causing chaos..." Records outlines as plan thoughts in tc-pilot.
      • LLM Output: record_thought (thought_chain_id=tc-pilot, thought_type=plan, content="Scene 1 Outline: ...").
    • (Repeats for key scenes)

13. Loops 19-M: Generating Script Sections & Recording Artifacts

    • AML -> LLM: Takes scene outlines one by one.
      • LLM Reasoning: "Generate dialogue and action for Scene 1 based on outline and character profiles (retrieved via search/context)." Updates plan: [{"description": "Generate Scene 1 script"}, {"description": "Record Scene 1 script artifact"}, ...].
      • LLM Output: record_thought (thought_type=reasoning, content="Writing Scene 1: INT. DSP WAITING ROOM - DAY...") [Self-correction: No, the output should be the action to generate, not the generation itself. The LLM just plans the next step here.]
      • LLM Output (Corrected): Updates plan to include generating Scene 1, perhaps using a hypothetical creative_writing:generate_scene tool or just storing the generated text in memory/thought. Let's assume it generates text and plans to store it.
      • LLM Reasoning (Internal to LLM): [Generates Scene 1 script text]
      • LLM Output: store_memory (memory_type=script_scene, content="[SCENE 1 SCRIPT TEXT]", description="Pilot Scene 1 Script").
    • AML (_execute_tool_call_internal): Stores Scene 1 text as a memory.
    • AML -> LLM (Next):
      • LLM Reasoning: "Scene 1 generated and stored. Need to create an artifact combining scenes as we go." Updates plan: [{"description": "Record Pilot Script Artifact (partial)"}, ...].
      • LLM Output: record_artifact (artifact_type=text, name="Pilot_Script_Draft_v1.txt", content="[SCENE 1 SCRIPT TEXT]", description="Ongoing pilot script draft").
    • AML (_execute_tool_call_internal): Creates/updates the artifact.
    • (Repeats for subsequent scenes, generating text, storing as memory, and updating the script artifact.)

Phase 4: Finalization (Loop K)

14. Loop K-1: Final Review and Formatting

    • AML -> LLM: All scenes generated and added to the artifact.
      • LLM Reasoning: "All pilot scenes generated. Retrieve full script artifact, perform final review for consistency and formatting." Updates plan: [{"description": "Get final script artifact content"}, {"description": "Record final formatted script artifact"}, ...].
      • LLM Output: get_artifact_by_id (artifact_id=ID of Pilot_Script_Draft_v1.txt).
    • AML (_execute_tool_call_internal): Retrieves artifact content.

15. Loop K: Record Final Artifact and Achieve Goal

    • AML (_gather_context): Context includes the full draft script content.
    • AML -> LLM:
      • LLM Reasoning: "Script retrieved. Apply standard screenplay formatting. [Internal formatting step]. Record final artifact. Goal is met." Updates plan.
      • LLM Output: record_artifact (artifact_type=file, name="Afterlife_Comedy_Pilot_FINAL.fdx", content="[FINAL FORMATTED SCRIPT]", description="Final Pilot Script", is_output=True). (Note: FDX format is illustrative).
    • AML (_execute_tool_call_internal): Executes record_artifact. Marks it as a workflow output.
    • AML -> LLM (Implicit Next):
      • LLM Output: "Goal Achieved: Developed novel 'Bureaucratic Afterlife Comedy' concept, created character profiles, outlined Season 1 arc, and generated the final formatted pilot script artifact (ID: [final_artifact_id])."
    • AML: Detects "Goal Achieved:", sets flag. Updates workflow status to completed.

16. Shutdown: Generates final report (likely including links to the concept memo, character profile memories, and the final script artifact). Saves state.

This creative workflow highlights:

• Structured Ideation: Using thoughts and memory to brainstorm and refine concepts.
• Modular Development: Using separate thought chains (tc-dev, tc-pilot) for different phases.
• Iterative Artifact Generation: Building the final script piece by piece, tracked via record_artifact.
• Leveraging Stored Knowledge: Using internal memory search (hybrid_search_memories) to inform creative choices.
• Semantic Memory: Storing core concepts, profiles, and arcs as reusable semantic knowledge.

Okay, let's dive deeper into the granular, low-level simulation of the creative task, emphasizing the specific mechanics of the UMS and AML interaction.

Task: Develop a creative, novel TV series concept, characters, arc, and pilot script.

Assumptions:

• Necessary search tools (corpus_search:fused_search, external_tools:check_concept_novelty) are available via MCP.
• AgentState starts empty or irrelevant.
• Base thresholds: Reflection=7, Consolidation=12.
• UMS DB Path: tv_concept_gen.db

Loop 1: Initialization & Workflow Creation

1. AML run starts: state = AgentState(), current_loop = 0. Calls initialize().
2. AML initialize: Loads empty state. Verifies * tools (assume OK). workflow_id is None.
3. AML run: current_loop = 1. Calls _gather_context().
4. AML _gather_context: Returns {"status": "No Active Workflow"}.
5. AML run: Sets plan based on context: state.current_plan = [PlanStep(id='step-a', description="Create workflow for goal: Develop original series concept...")]. state.needs_replan = False.
6. AML run: Calls _call_agent_llm(goal, context).
7. AML _call_agent_llm: Constructs prompt: System instructions + User prompt (goal, minimal context, explicit instruction to create workflow first). Sends to Claude 3.7 Sonnet.
8. LLM Response (Simulated):
   • Text: "Okay, the goal is creative development. First, I need a workflow to track everything. Reasoning: Setting up the project structure. Updated Plan:\njson\n[{\"id\": \"step-b\", \"description\": \"Brainstorm potential genres and high-level concepts\", \"status\": \"planned\"}]\n"
   • Tool Call: {"name": "create_workflow", "input": {"title": "Novel TV Concept Gen", "goal": "Develop original series concept, characters, arc, pilot script"}}
9. AML _call_agent_llm: Parses response. Extracts tool call and the updated_plan_steps. Returns {"decision": "call_tool", "tool_name": "create_workflow", "arguments": {...}, "updated_plan_steps": [PlanStep(id='step-b', ...)]}.
10. AML run: agent_decision received. Checks updated_plan_steps: Valid list of PlanStep found. Updates state.current_plan = [PlanStep(id='step-b', ...)]. needs_replan = False.
11. AML run: decision_type is call_tool. Calls _execute_tool_call_internal(tool_name="create_workflow", arguments={...}, record_action=True, planned_dependencies=None).
12. AML _execute_tool_call_internal:
    • Finds server for create_workflow.
    • Checks planned_dependencies: None.
    • should_record_start = True (it's not on the exclusion list). Calls _record_action_start_internal.
    • AML _record_action_start_internal: Finds record_action_start tool. Executes it (workflow_id=None initially, title="Exec: create_workflow"). Returns action_id="act-wfcrt". Wait, can't record start without WF ID. _record_action_start_internal should handle this or _execute_tool_call_internal should skip recording for create_workflow. Correction: Assume _execute_tool_call_internal skips recording for create_workflow.
    • Injects workflow_id=None (correct for this tool). Executes create_workflow.
    • UMS create_workflow Tool: Generates workflow_id="wf-tv123", thought_chain_id="tc-main0". Inserts into workflows, thought_chains. Commits. Returns {"success": True, "workflow_id": "wf-tv123", "primary_thought_chain_id": "tc-main0", ...}.
    • tool_result_content received. success=True. last_action_summary updated. last_error_details = None. consecutive_error_count = 0.
    • No background tasks triggered by create_workflow.
    • Calls _handle_workflow_side_effects.
    • AML _handle_workflow_side_effects: Matches create_workflow. Updates state.workflow_id="wf-tv123", state.context_id="wf-tv123", state.workflow_stack=["wf-tv123"], state.current_thought_chain_id="tc-main0". Logs switch. Resets plan based on side effect handler: state.current_plan = [PlanStep(description="Start new workflow: Novel TV Concept Gen...")]. Correction: Side effect handler should likely not overwrite plan if LLM provided one. Revised: Side effect handler sets IDs, leaves plan from LLM intact. state.current_plan remains [PlanStep(id='step-b', ...)].
    • Returns tool_result_content.
13. AML run: Skips heuristic _update_plan because LLM provided one.
14. AML run: Calls _run_periodic_tasks. Counters are low, nothing happens.
15. AML run: Loop ends. Saves state. Sleeps.

Loop 2: Brainstorm Planning

1. AML run: current_loop = 2. Calls _gather_context().
2. AML _gather_context: Fetches core context for wf-tv123 (title, goal, status=active). working_memory likely empty. proactive_memories search for plan step "Brainstorm..." runs, likely finds little. Returns context.
3. AML run: Calls _call_agent_llm.
4. AML _call_agent_llm: Constructs prompt (goal, context incl. wf-tv123, tc-main0, plan step "Brainstorm...").
5. LLM Response:
   • Text: "Okay, brainstorming concepts. Need inspiration and structure. Let's check memory for common successful patterns and maybe unusual ideas. Then log the brainstormed concepts as thoughts. Updated Plan:\njson\n[{\"id\": \"step-c\", \"description\": \"Search memory for tropes/combos\", \"status\": \"planned\"}, {\"id\": \"step-d\", \"description\": \"Record brainstormed ideas as thoughts\", \"status\": \"planned\", \"depends_on\": [\"act-search1\"]}]\n" (LLM anticipates the action ID of the search step)
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-main0", "thought_type": "plan", "content": "Plan brainstorm: 1. Search memory for tropes/combos. 2. Record resulting ideas as thoughts."}} (LLM decides to record the plan first)
6. AML _call_agent_llm: Parses. Returns {"decision": "call_tool", "tool_name": "record_thought", "arguments": {...}, "updated_plan_steps": [PlanStep(id='step-c', ...), PlanStep(id='step-d', ...)]}.
7. AML run: Updates state.current_plan from LLM.
8. AML run: Executes record_thought.
9. AML _execute_tool_call_internal:
   • Skips recording start/completion for record_thought.
   • Injects workflow_id, thought_chain_id. Executes tool.
   • UMS record_thought Tool: Inserts thought th-plan1 into thoughts (seq 1 in tc-main0). Inserts linked memory mem-plan1 into memories (type=REASONING_STEP). Returns {"success": True, "thought_id": "th-plan1", "linked_memory_id": "mem-plan1"}.
   • success=True. last_action_summary updated.
   • Trigger: _run_auto_linking scheduled for mem-plan1.
   • Returns result.
10. AML run: Background task _run_auto_linking starts for mem-plan1 (likely finds few links).
11. AML run: Calls _run_periodic_tasks. Nothing significant triggered yet. Loop ends. Saves state. Sleeps.

Loop 3: Memory Search Execution

1. AML run: current_loop = 3. Calls _gather_context. Context includes th-plan1.
2. AML run: Current plan step step-c: "Search memory for tropes/combos".
3. AML run: Calls _call_agent_llm.
4. LLM Response:
   • Text: "Executing plan step 'Search memory'. Using hybrid search for broad coverage. Updated Plan: [Same as before, step-c status unchanged for now]" (LLM might not mark step completed until result is processed)
   • Tool Call: {"name": "hybrid_search_memories", "input": {"workflow_id": "wf-tv123", "query": "successful TV show tropes OR underused genre combinations", "limit": 10, "include_content": False}}
5. AML _call_agent_llm: Parses. Returns decision.
6. AML run: LLM didn't update plan, keeps current.
7. AML run: Executes hybrid_search_memories.
8. AML _execute_tool_call_internal:
   • should_record_start = False.
   • Checks dependencies for step-c: None.
   • Injects workflow_id. Executes tool.
   • UMS hybrid_search_memories Tool:
     • Calls _find_similar_memories (semantic search part).
     • Performs FTS query on memory_fts.
     • Combines scores, ranks, limits results. Fetches details. Updates access stats (_update_memory_access, _log_memory_operation type hybrid_access). Commits access updates.
     • Returns {"success": True, "memories": [{"memory_id": "mem-trope1", ...}, ...], "total_candidates_considered": X}.
   • success=True. last_action_summary updated with result preview. consecutive_error_count = 0.
   • Triggers _check_and_trigger_promotion for accessed memories (likely no effect yet).
   • Returns result.
9. AML run: Calls heuristic _update_plan.
10. AML _update_plan: Plan step step-c was successful. Updates step-c.status = "completed", step-c.result_summary = "Success: Found X memories...". Removes step-c from plan. Plan is now [PlanStep(id='step-d', description="Record brainstormed ideas...", depends_on=['act-search1'])]. (Heuristic needs mechanism to know the action ID, maybe _execute_tool_call_internal should return it if recorded). Refinement: Let's assume the LLM should have proposed marking step-c complete and provided the actual action ID for step-d's dependency in its next reasoning step.

Loop 4: Recording Brainstormed Ideas (Reacting to Search)

1. AML run: current_loop = 4. Calls _gather_context. Context includes results from hybrid_search_memories.
2. AML run: Current plan step step-d: "Record brainstormed ideas...". Dependency: act-search1 (hypothetical ID from LLM's previous plan).
3. AML run: Calls _call_agent_llm.
4. LLM Response:
   • Text: "Search results analyzed (tropes, etc.). Generating novel concepts based on results and goal. Idea 1: 'Bureaucratic Afterlife Comedy'. Idea 2: 'Historical Assistants Drama'. Idea 3: 'Memory Object Sci-Fi'. Recording first idea. Updated Plan:\njson\n[{\"id\": \"step-d\", \"description\": \"Record brainstormed ideas as thoughts\", \"status\": \"in_progress\"}, {\"id\": \"step-e\", \"description\": \"Select most promising concept\", \"status\": \"planned\", \"depends_on\": [\"act-record-idea3\"]}]\n" (LLM marks step 'in_progress', anticipates multiple record actions)
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-main0", "thought_type": "hypothesis", "content": "Idea 1: Bureaucratic Afterlife Comedy."}}
5. AML _call_agent_llm: Parses. Returns decision.
6. AML run: Updates plan (status of step-d, adds step-e).
7. AML run: Executes record_thought for Idea 1. Dependency check for step-d uses act-search1. Assume _check_prerequisites uses get_action_details on act-search1 ID, finds it completed. Proceeds.
8. AML _execute_tool_call_internal: Executes tool.
9. UMS: Stores thought th-idea1, linked memory mem-idea1.
10. AML: Background link task for mem-idea1.
11. AML run: Calls heuristic _update_plan. Since the LLM updated the plan and the step is in_progress, the heuristic might do nothing or just log progress. Plan remains [step-d (in_progress), step-e].

Loops 5 & 6: Recording Ideas 2 & 3

• Similar to Loop 4, but the LLM calls record_thought for Idea 2, then Idea 3.
• The LLM reasoning might update the depends_on for step-e to the action ID associated with recording the last idea (act-record-idea3).
• After Loop 6, step-d might be marked completed by the LLM or the heuristic update. Plan becomes [step-e].

Loop 7: Selecting Concept

1. AML run: current_loop = 7. Context includes the 3 idea thoughts/memories. Plan step step-e: "Select concept".
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Evaluating concepts... [Reasoning as before]... Decision: Pursue 'Bureaucratic Afterlife Comedy'. Recording decision. Updated Plan:\njson\n[{\"id\": \"step-f\", \"description\": \"Store core concept memory\", \"status\": \"planned\", \"depends_on\": [\"act-record-decision\"]}, {\"id\": \"step-g\", \"description\": \"Check concept novelty\", \"status\": \"planned\", \"depends_on\": [\"act-store-concept\"]}]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-main0", "thought_type": "decision", "content": "Selected concept: Bureaucratic Afterlife Comedy."}}
4. AML: Updates plan. Executes record_thought.
5. UMS: Stores th-decision1, mem-decision1.
6. AML: Background link task for mem-decision1. Success counters increment.

Loop 8: Storing Core Concept Memory

1. AML run: current_loop = 8. Plan step-f: "Store core concept". Dependency act-record-decision.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Storing the chosen concept details as a semantic memory for robustness." Updated Plan: [Marks step-f in progress, step-g depends on act-store-concept]
   • Tool Call: {"name": "store_memory", "input": {"workflow_id": "wf-tv123", "memory_level": "semantic", "memory_type": "fact", "content": "TV Concept: Slice-of-life comedy set in...", "importance": 8.0, "confidence": 0.7}}
4. AML: Updates plan. Executes store_memory (checks dependency act-record-decision first).
5. AML _execute_tool_call_internal: Records action start (act-store-concept). Executes tool. Records completion.
6. UMS: Stores mem-concept1, generates embedding.
7. AML: Background linking for mem-concept1. Heuristic updates plan (removes completed step-f). Plan is now [step-g].

Loop 9: Checking Novelty

1. AML run: current_loop = 9. Plan step-g: "Check novelty". Dependency act-store-concept.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Checking novelty using external tool." Updated Plan: [Marks step-g in progress]
   • Tool Call: {"name": "external_tools:check_concept_novelty", "input": {"query": "TV shows bureaucratic afterlife comedy"}}
4. AML: Updates plan. Executes check_concept_novelty (checks dependency).
5. AML _execute_tool_call_internal: Records start (act-check-novelty). Executes simulated tool. Gets result "Low overlap...". Records completion with result.
6. AML: Heuristic updates plan (removes completed step-g). Plan might now be empty, triggering LLM to plan next phase (Character Dev).

Okay, let's continue the detailed simulation, picking up right after the concept novelty check (end of Loop 9). The current plan is likely empty, prompting the LLM to plan the next phase. current_thought_chain_id is still tc-main0.

Phase 2: Developing the Premise, Characters, and Arc (Loops 10-19)

Loop 10: Planning Premise Development & Thought Chain Creation

1. AML run: current_loop = 10. Calls _gather_context. Context includes validated concept (mem-concept1) and successful novelty check action (act-check-novelty). state.current_plan is empty.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Concept ('Bureaucratic Afterlife Comedy') is selected and validated. Next, develop the core premise details (setting, tone, conflict). To keep this organized, I'll create a dedicated thought chain for development. Updated Plan:\njson\n[{\"id\": \"step-h\", \"description\": \"Create 'Afterlife Comedy Dev' thought chain\", \"status\": \"planned\"}, {\"id\": \"step-i\", \"description\": \"Record detailed premise in new chain\", \"status\": \"planned\", \"depends_on\": [\"act-create-dev-chain\"]}]\n"
   • Tool Call: {"name": "create_thought_chain", "input": {"workflow_id": "wf-tv123", "title": "Afterlife Comedy Dev"}}
4. AML _call_agent_llm: Parses. Returns decision.
5. AML run: Updates state.current_plan from LLM.
6. AML run: Executes create_thought_chain. Plan step step-h. No dependencies.
7. AML _execute_tool_call_internal:
   • Skips recording start/completion for create_thought_chain.
   • Injects workflow_id. Executes tool.
   • UMS create_thought_chain Tool: Generates thought_chain_id="tc-dev1". Inserts into thought_chains. Commits. Returns {"success": True, "thought_chain_id": "tc-dev1", ...}. Assume action ID is act-create-dev-chain.
   • success=True. last_action_summary updated.
   • Calls _handle_workflow_side_effects.
   • AML _handle_workflow_side_effects: Matches create_thought_chain. Updates state.current_thought_chain_id="tc-dev1". Logs switch.
   • Returns result.
8. AML run: Calls heuristic _update_plan. Marks step-h completed. Plan is now [step-i].
9. AML run: Calls _run_periodic_tasks. Nothing significant. Loop ends. Saves state. Sleeps.

Loop 11: Recording Detailed Premise

1. AML run: current_loop = 11. Context includes current_thought_chain_id="tc-dev1". Plan step step-i: "Record premise". Dependency act-create-dev-chain.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Now in the 'Afterlife Comedy Dev' chain (tc-dev1). Recording the detailed premise developed previously: Setting: 'The Intermediate Plane - Department of Soul Processing (DSP)'. Tone: 'The Office' meets existentialism. Conflict: Incompetent management, arcane rules, soul glitches. Updated Plan:\njson\n[{\"id\": \"step-j\", \"description\": \"Plan protagonist character (Alex Chen)\", \"status\": \"planned\", \"depends_on\": [\"act-record-premise\"]}]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-dev1", "thought_type": "summary", "content": "Premise: Comedy set in the DSP, mixing mundane office humor (The Office style) with existential themes. Core conflict stems from bureaucratic inefficiency, arbitrary rules, inter-departmental friction, and occasional 'soul glitches'."}}
4. AML _call_agent_llm: Parses. Returns decision.
5. AML run: Updates state.current_plan.
6. AML run: Executes record_thought. Checks dependency act-create-dev-chain (completed). Proceeds.
7. AML _execute_tool_call_internal: Executes tool. Assume action ID is act-record-premise.
8. UMS: Stores thought th-premise1 in tc-dev1. Creates linked memory mem-premise1.
9. AML: Background linking for mem-premise1. Heuristic updates plan (removes completed step-i). Plan is now [step-j].
10. AML run: Loop ends. Saves state. Sleeps.

Loop 12: Planning Protagonist (Alex Chen)

1. AML run: current_loop = 12. Plan step-j: "Plan protagonist". Dependency act-record-premise.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Developing the protagonist. Name: Alex Chen. Role: Recently deceased human, former accountant. Personality: Orderly, anxious, easily overwhelmed by the absurdity and inefficiency of the DSP. Initial Goal: Simply understand the rules and survive the afterlife onboarding process. Updated Plan:\njson\n[{\"id\": \"step-k\", \"description\": \"Record Alex Chen character thought\", \"status\": \"planned\", \"depends_on\": [\"act-record-premise\"]}, {\"id\": \"step-l\", \"description\": \"Store Alex Chen profile memory\", \"status\": \"planned\", \"depends_on\": [\"act-record-alex-thought\"]}]\n" (LLM plans two steps: record thought, then store memory)
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-dev1", "thought_type": "hypothesis", "content": "Character Concept: Alex Chen (Protagonist). Accountant, newly deceased. Anxious, seeks order in chaotic DSP. Initial motivation: Survival/understanding."}}
4. AML: Updates plan (replaces step-j with step-k, step-l). Executes record_thought. Checks dependency act-record-premise (completed).
5. AML _execute_tool_call_internal: Executes tool. Action ID act-record-alex-thought.
6. UMS: Stores thought th-alex1, memory mem-alex-idea1.
7. AML: Background linking. Heuristic updates plan (removes step-k). Plan is [step-l].

Loop 13: Storing Protagonist Profile Memory

1. AML run: current_loop = 13. Plan step-l: "Store Alex Chen profile". Dependency act-record-alex-thought.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Storing the detailed Alex Chen profile as a semantic memory for easy retrieval later during scriptwriting." Updated Plan: [{"id": "step-m", "description": "Plan supervisor character", "status": "planned", "depends_on": ["act-store-alex-mem"]}]
   • Tool Call: {"name": "store_memory", "input": {"workflow_id": "wf-tv123", "memory_level": "semantic", "memory_type": "character_profile", "content": "{\"name\": \"Alex Chen\", \"role\": \"Protagonist\", \"background\": \"Mid-level accountant, died unexpectedly (e.g., rogue filing cabinet incident)\", \"personality\": \"Organized, detail-oriented, anxious, seeks logic, dry wit under stress\", \"initial_goal\": \"Navigate DSP onboarding, find decent coffee\"}", "description": "Character Profile: Alex Chen (Protagonist)", "importance": 7.5, "confidence": 0.85, "tags": ["character", "protagonist", "human"]}}
4. AML: Updates plan. Executes store_memory. Checks dependency act-record-alex-thought (completed).
5. AML _execute_tool_call_internal: Records start (act-store-alex-mem). Executes tool. Records completion.
6. UMS: Stores memory mem-alex-profile, generates embedding.
7. AML: Background linking (might link mem-alex-profile to mem-concept1). Heuristic updates plan (removes step-l). Plan is [step-m].

Loops 14-17: Developing Supporting Characters (Supervisor & Intern)

• Loop 14 (Plan Supervisor): LLM plans supervisor character (e.g., "Bartholomew, cynical centuries-old angel, middle-manager"). Records thought th-sup1. Plan becomes [step-n (Store Sup Mem)].
• Loop 15 (Store Supervisor): LLM calls store_memory for Bartholomew (mem-sup-profile). Plan becomes [step-o (Plan Intern)]. Background linking connects mem-sup-profile.
• Loop 16 (Plan Intern): LLM plans intern character (e.g., "Fizzle, overly enthusiastic demon intern from 'Minor Temptations' dept, causes chaos"). Records thought th-int1. Plan becomes [step-p (Store Int Mem)].
• Loop 17 (Store Intern): LLM calls store_memory for Fizzle (mem-int-profile). Plan becomes [step-q (Plan Arc)]. Background linking connects mem-int-profile.

(Optional Linking Step): The LLM might insert a plan step here: "Link character profiles to main concept". It would then call create_memory_link multiple times (source=mem-alex-profile, target=mem-concept1, type=related, etc.).

Loop 18: Planning Season 1 Arc

1. AML run: current_loop = 18. Context includes character profiles. Plan step-q: "Plan Arc". Dependency likely act-store-int-mem. current_thought_chain_id = tc-dev1.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Characters established. Now outlining the Season 1 story arc within the dev chain (tc-dev1). Arc: Alex arrives, struggles, forms alliances with Bartholomew/Fizzle against inefficient system/minor antagonist (e.g., rival dept head), uncovers source of a recurring 'soul glitch', achieves a small victory improving their section's process, gains grudging respect. Updated Plan:\njson\n[{\"id\": \"step-r\", \"description\": \"Record Season 1 Arc thought\", \"status\": \"planned\", \"depends_on\": [\"act-store-int-mem\"]}, {\"id\": \"step-s\", \"description\": \"Store Season 1 Arc memory\", \"status\": \"planned\", \"depends_on\": [\"act-record-arc-thought\"]}]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-dev1", "thought_type": "plan", "content": "S1 Arc Outline: 1. Arrival/Confusion. 2. Intro Supervisor/Intern. 3. Initial Task/Failure. 4. Unlikely Alliance Formation. 5. Investigate 'Soul Glitch'. 6. Confront Rival/Obstacle. 7. Minor System Improvement/Resolution. 8. Finale: Tentative acceptance/new status quo."}}
4. AML: Updates plan. Executes record_thought. Checks dependency.
5. AML _execute_tool_call_internal: Executes tool. Action ID act-record-arc-thought.
6. UMS: Stores th-arc1, mem-arc-idea1.
7. AML: Background linking. Heuristic updates plan (removes step-r). Plan [step-s].

Loop 19: Storing Season 1 Arc Memory

1. AML run: current_loop = 19. Plan step-s: "Store Arc memory". Dependency act-record-arc-thought.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Storing the Season 1 arc details as a semantic memory." Updated Plan: [{"id": "step-t\", \"description\": \"Plan Pilot Episode structure\", \"status\": \"planned\", \"depends_on\": [\"act-store-arc-mem\"]}] (Planning moves towards pilot)
   • Tool Call: {"name": "store_memory", "input": {"workflow_id": "wf-tv123", "memory_level": "semantic", "memory_type": "story_arc", "content": "Season 1 Arc: [Full arc details from thought th-arc1]", "description": "Season 1 Story Arc for Afterlife Comedy", "importance": 7.5, "confidence": 0.8, "tags": ["arc", "season1", "plot"]}}
4. AML: Updates plan. Executes store_memory. Checks dependency.
5. AML _execute_tool_call_internal: Records start (act-store-arc-mem). Executes tool. Records completion. Also calls create_memory_link (source=act-store-arc-mem, target=mem-concept1, type=related).
6. UMS: Stores mem-arc-profile, generates embedding. Stores link.
7. AML: Background linking for mem-arc-profile. Heuristic updates plan (removes step-s). Plan is [step-t].

Loop 20: Potential Reflection

1. AML run: current_loop = 20. Let's assume successful_actions_since_reflection (now maybe around 10-12) exceeds current_reflection_threshold (still 7).
2. AML run: Calls _run_periodic_tasks.
3. AML _run_periodic_tasks: Schedules generate_reflection (type='strengths', cycle index 1). Executes it.
4. UMS: Provides logs (chain creation, thoughts recorded, memories stored for concept/chars/arc).
5. LLM (in reflection): "Strengths: Systematic development using dedicated chain (tc-dev1). Clear concept, distinct character archetypes (Alex, Bartholomew, Fizzle), logical season arc progression stored."
6. UMS: Stores reflection ref-strength1.
7. AML: last_meta_feedback set. successful_actions_since_reflection = 0.
8. AML run: Calls _call_agent_llm. Context includes the positive feedback. Plan is [step-t].
9. LLM Response:
   • Text: "Positive reflection noted. Proceeding to plan pilot episode structure. This requires a new thought chain for script details. Updated Plan:\njson\n[{\"id\": \"step-u\", \"description\": \"Create 'Pilot Script Gen' thought chain\", \"status\": \"planned\", \"depends_on\": [\"act-store-arc-mem\"]}, {\"id\": \"step-v\", \"description\": \"Outline Pilot Scene 1\", \"status\": \"planned\", \"depends_on\": [\"act-create-pilot-chain\"]}]\n"
   • Tool Call: {"name": "create_thought_chain", "input": {"workflow_id": "wf-tv123", "title": "Pilot Script Gen"}}
10. AML: Updates plan. Executes create_thought_chain. Checks dependency.
11. AML _execute_tool_call_internal: Executes tool. Action ID act-create-pilot-chain.
12. UMS: Creates tc-pilot1.
13. AML _handle_workflow_side_effects: Updates state.current_thought_chain_id="tc-pilot1".
14. AML: Heuristic updates plan (removes step-u). Plan [step-v].

Phase 3: Pilot Script Generation (Loops 21-30)

Loop 21: Outline Scene 1

1. AML run: current_loop = 21. current_thought_chain_id="tc-pilot1". Plan step-v: "Outline Pilot Scene 1". Dependency act-create-pilot-chain.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Planning Scene 1 for the pilot in chain tc-pilot1. Goal: Introduce Alex, establish bewildered tone, show sterile environment. Outline: INT. DSP WAITING ROOM - DAY. Sterile, beige, fluorescent lights. Alex appears, confused. Reads baffling 'Welcome to the Intermediate Plane' pamphlet. Updated Plan:\njson\n[{\"id\": \"step-w\", \"description\": \"Record Scene 1 Outline thought\", \"status\": \"planned\", \"depends_on\": [\"act-create-pilot-chain\"]}, {\"id\": \"step-x\", \"description\": \"Outline Pilot Scene 2\", \"status\": \"planned\", \"depends_on\": [\"act-record-sc1-outline\"]}]\n"
   • Tool Call: {"name": "record_thought", "input": {"workflow_id": "wf-tv123", "thought_chain_id": "tc-pilot1", "thought_type": "plan", "content": "Scene 1 Outline: INT. DSP WAITING ROOM - DAY. Sterile setting. Alex appears, confused. Interacts with confusing pamphlet."}}
4. AML: Updates plan (replaces step-v). Executes record_thought. Checks dependency.
5. AML _execute_tool_call_internal: Executes tool. Action ID act-record-sc1-outline.
6. UMS: Stores th-sc1-outline.
7. AML: Heuristic updates plan (removes step-w). Plan [step-x].

Loops 22 & 23: Outline Scenes 2 & 3

• Similar process: LLM outlines Scene 2 (Meeting Bartholomew) and Scene 3 (Meeting Fizzle, initial task/problem), recording thoughts th-sc2-outline, th-sc3-outline in tc-pilot1. Plan advances. After Loop 23, plan might be [step-aa (Generate Scene 1 Script)].

Loop 24: Generate Scene 1 Script Text (Memory)

1. AML run: current_loop = 24. Plan step-aa: "Generate Scene 1 Script". Dependency act-record-sc3-outline.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Generating script text for Scene 1 based on outline (th-sc1-outline) and Alex's profile (mem-alex-profile). Storing result as memory first. Updated Plan:\njson\n[{\"id\": \"step-bb\", \"description\": \"Store Scene 1 Script memory\", \"status\": \"planned\", \"depends_on\": [\"act-record-sc3-outline\"]}, {\"id\": \"step-cc\", \"description\": \"Record Pilot Script Artifact (v1)\", \"status\": \"planned\", \"depends_on\": [\"act-store-sc1-mem\"]}]\n"
   • Tool Call: {"name": "store_memory", "input": {"workflow_id": "wf-tv123", "memory_level": "episodic", "memory_type": "script_scene", "content": "[GENERATED SCRIPT TEXT FOR SCENE 1 - Approx 3 pages]", "description": "Pilot Script: Scene 1", "importance": 6.0, "confidence": 0.9, "tags": ["script", "scene1", "pilot"], "action_id": "act-gen-sc1" }} (LLM anticipates action ID)
4. AML: Updates plan (replaces step-aa). Executes store_memory. Checks dependency.
5. AML _execute_tool_call_internal: Records start (act-store-sc1-mem). Executes tool. Records completion.
6. UMS: Stores memory mem-sc1. Generates embedding.
7. AML: Background linking. Heuristic updates plan (removes step-bb). Plan [step-cc].

Loop 25: Record Draft Artifact (v1)

1. AML run: current_loop = 25. Plan step-cc: "Record Artifact (v1)". Dependency act-store-sc1-mem.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Creating the initial draft artifact containing Scene 1." Updated Plan: [{"id\": \"step-dd\", \"description\": \"Generate Scene 2 Script memory\", \"status\": \"planned\", \"depends_on\": [\"act-record-art1\"]}]
   • Tool Call: {"name": "record_artifact", "input": {"workflow_id": "wf-tv123", "artifact_type": "text", "name": "Pilot_Script_Draft_v1.txt", "content": "[CONTENT FROM mem-sc1]", "description": "Pilot script draft, scenes 1", "tags": ["script", "draft", "pilot"], "action_id": "act-record-art1"}} (LLM uses content from memory)
4. AML: Updates plan. Executes record_artifact. Checks dependency.
5. AML _execute_tool_call_internal: Records start (act-record-art1). Executes tool. Records completion. record_artifact returns artifact_id="art-draft1" and linked_memory_id.
6. UMS: Stores artifact art-draft1. Stores linked memory about artifact creation.
7. AML: Background linking for artifact memory. Heuristic updates plan (removes step-cc). Plan [step-dd].

Loops 26-29: Generate Scenes 2 & 3, Update Artifact

• Loop 26 (Gen Sc 2): LLM calls store_memory for Scene 2 script text (mem-sc2). Plan advances.
• Loop 27 (Update Art v2):
  • LLM plans to update artifact. Plan: [Get art-draft1, Update art-draft1]
  • LLM calls get_artifact_by_id (artifact_id=art-draft1).
  • Next Loop: LLM receives Scene 1 text. Calls record_artifact again with same name but new content (Scene 1 + Scene 2). UMS record_artifact should ideally handle UPSERT logic or the LLM should plan to delete old/create new. Assumption: record_artifact replaces content if name exists or LLM plans delete/create. Plan advances to generate Scene 3.
• Loop 28 (Gen Sc 3): LLM calls store_memory for Scene 3 text (mem-sc3). Plan advances.
• Loop 29 (Update Art v3): LLM gets artifact, combines Scene 1+2+3, calls record_artifact to update art-draft1 with full content. Plan advances to final review.

Phase 4: Finalization (Loops 30-32)

Loop 30: Plan Final Review & Formatting

1. AML run: current_loop = 30. Script generated, stored in art-draft1. Plan is likely [step-ff (Final Review)].
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Pilot script scenes generated and combined in draft artifact art-draft1. Need to retrieve the full content, perform a final consistency check, apply standard screenplay formatting, and save as the final output artifact. Updated Plan:\njson\n[{\"id\": \"step-gg\", \"description\": \"Get final draft script content\", \"status\": \"planned\", \"depends_on\": [\"act-update-art3\"]}, {\"id\": \"step-hh\", \"description\": \"Record FINAL formatted script artifact\", \"status\": \"planned\", \"depends_on\": [\"act-get-draft\"]}]\n"
   • Tool Call: {"name": "get_artifact_by_id", "input": {"artifact_id": "art-draft1", "include_content": true}}
4. AML: Updates plan. Executes get_artifact_by_id. Checks dependency act-update-art3.
5. AML _execute_tool_call_internal: Executes tool. Action ID act-get-draft.
6. UMS: Returns full content of art-draft1.
7. AML: Heuristic updates plan (removes step-gg). Plan [step-hh].

Loop 31: Record Final Formatted Script Artifact

1. AML run: current_loop = 31. Context includes full draft script. Plan step-hh: "Record FINAL artifact". Dependency act-get-draft.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Retrieved full draft. [Internal LLM step: Applying standard screenplay formatting - Headers, Dialogue, Action lines]. Saving the final version as a distinct output artifact. Goal should be met after this. Updated Plan: []" (LLM empties plan as this is final step)
   • Tool Call: {"name": "record_artifact", "input": {"workflow_id": "wf-tv123", "artifact_type": "file", "name": "Afterlife_Comedy_Pilot_FINAL.txt", "content": "[FINAL FORMATTED SCRIPT TEXT]", "description": "Final Pilot Script - Formatted", "is_output": true, "tags": ["script", "final", "pilot", "output"], "action_id": "act-record-final"}}
4. AML: Updates plan (empty). Executes record_artifact. Checks dependency act-get-draft.
5. AML _execute_tool_call_internal: Records start (act-record-final). Executes tool. Records completion. Returns artifact_id="art-final1".
6. UMS: Stores art-final1 artifact, marks is_output=True. Stores linked memory.
7. AML: Background linking for final artifact memory. Heuristic updates plan (plan is already empty).

Loop 32: Signal Goal Completion

1. AML run: current_loop = 32. Plan is empty. last_action_summary indicates final artifact saved.
2. AML run: Calls _call_agent_llm.
3. LLM Response:
   • Text: "Goal Achieved: Successfully developed the 'Bureaucratic Afterlife Comedy' concept, created character profiles (Alex, Bartholomew, Fizzle), outlined the Season 1 story arc, and generated the final formatted pilot script artifact (ID: art-final1)."
   • Tool Call: None.
4. AML _call_agent_llm: Parses. Returns {"decision": "complete", "summary": "..."}.
5. AML run: decision_type is complete. Sets state.goal_achieved_flag = True. Calls _update_workflow_status_internal("completed", "..."). Loop terminates.

Phase 5: Shutdown

1. AML: Calls _generate_final_report.
2. AML (_generate_final_report): Calls generate_workflow_report tool.
3. UMS: Provides data. Report is generated (Markdown).
4. AML: Prints report. Saves final state. Cleans up background tasks. Exits.

This detailed walkthrough shows the back-and-forth, the incremental building of knowledge and artifacts within the UMS, the crucial role of planning and dependency checking, the use of dedicated thought chains for organization, and how meta-cognition can interleave with the creative process. The level of detail managed by the UMS allows the agent to tackle such a multi-faceted creative project in a structured way.