# πŸ—ΊοΈ AI Agent Development Work Plan **Date:** January 3, 2026 **Status:** βœ… Phase 1 - Foundation & Infrastructure (100% Complete) **Current Task:** Phase 3 - Core AI Agent (3.1) - **NEXT** ## 🎯 Project Goal Build a fully functional LLM-based AI agent that can play Settlers of Catan autonomously, making intelligent strategic decisions and interacting naturally with other players. --- ## πŸ“Š Development Phases > **Note:** Phase 4 (Monitoring & Debugging) should be developed **early and in parallel** with Phase 3. > The web dashboard and logging are **critical** for observing agent behavior during development! ### Phase 1: Foundation & Infrastructure πŸ—οΈ **Goal:** Build the core infrastructure needed to support AI agents #### 1.1 Configuration Management βœ… **COMPLETED** - [x] Create centralized configuration system - [x] LLM settings (model, temperature, max_tokens, etc.) - [x] API credentials management - [x] Agent parameters (custom instructions only) - [x] Performance settings (timeouts, retries, caching) - [x] Create config file format (YAML) - [x] Build configuration loader and validator - [x] Add environment variable support for sensitive data **Files created:** - βœ… `pycatan/ai/config.py` - Configuration management - βœ… `pycatan/ai/config_example.yaml` - Example configuration file - βœ… `pycatan/ai/config_dev.yaml` - Default dev configuration - βœ… `.env.example` - Environment variables template --- #### 1.2 Prompt Management Layer βœ… **COMPLETED** - [x] Design prompt processing pipeline - [x] Implement game state filtering - [x] Hide opponent's private information - [x] Filter development cards - [x] Remove non-visible game elements - [x] Build perspective transformation - [x] Convert game state to agent's viewpoint - [x] Format resources and points - [x] Present relative positioning - [x] Create prompt template system - [x] Meta data section - [x] Task context section - [x] Game state section - [x] Social context section - [x] Memory section - [x] Constraints section - [x] Build custom instruction injection per agent **Files created:** - βœ… `pycatan/ai/prompt_manager.py` - Main prompt processing - βœ… `pycatan/ai/state_filter.py` - Game state filtering logic - βœ… `pycatan/ai/prompt_templates.py` - Template definitions --- #### 1.2.5 Game State Optimization βœ… **COMPLETED** **Goal:** Optimize the game state capture and representation for better LLM consumption - [x] Review current game state structure from `play_and_capture.py` - [x] Design improved game state format - [x] Compress player information structure - [x] Improve board representation (lookup tables H & N) - [x] Add resource/harbor code mappings - [x] Reduce redundancy and token usage (removed pixel_coords, board_graph) - [x] Add status flags (Longest Road, Largest Army) - [x] Create optimized state format with legend - [x] Update game state capture to save both formats (.json + .txt) - [x] Fix timing: capture state at turn START (not just after actions) - [x] Test with real game scenarios **Files modified:** - βœ… `examples/ai_testing/play_and_capture.py` - Optimized state capture - βœ… `pycatan/management/game_manager.py` - Added state capture at turn start **Key achievements:** - 🎯 State representation optimized by ~60% (removed redundant fields) - πŸ“Š Compressed format with lookup tables (H=hexes, N=nodes) - πŸ”„ Real-time state updates at `current_state_optimized.txt` - πŸ“ Clear legend/documentation included in output - βœ… Captures state at decision point (turn start) --- #### 1.3 Response Parser βœ… **COMPLETED** - [x] Define structured response format (JSON schema) - [x] Build response parser and validator - [x] Implement error handling for malformed responses - [x] Create fallback mechanisms for parsing failures - [x] Add response logging for debugging **Files created:** - βœ… `pycatan/ai/response_parser.py` - Parse and validate LLM responses - βœ… `pycatan/ai/schemas.py` - JSON schemas for requests/responses **Key features:** - 🎯 Dual schema support: Active turn (with action) & Observing (no action) - πŸ›‘οΈ Error handling: Invalid JSON, missing fields, type validation - πŸ”§ Fallback mechanisms: JSON repair, structure repair, default values - πŸ“Š Parse statistics tracking - πŸ” Flexible parsing: Handles markdown code blocks, extra text - βœ… Action parameter validation against expected schemas --- ### Phase 2: Memory System 🧠 **Goal:** Enable agents to maintain context and learning across turns #### 2.1 Memory Structure - [ ] Design memory data model - [ ] Short-term observations (last N turns) - [ ] Strategic notes (persistent) - [ ] Social tracking (player relationships) - [ ] Game insights (patterns observed) - [ ] Implement memory storage (in-memory for now) - [ ] Build memory retrieval and formatting **Files to create:** - `pycatan/ai/memory.py` - Memory management system --- #### 2.2 Memory Operations - [ ] Add note creation and updates - [ ] Implement memory pruning (keep relevant, remove old) - [ ] Build memory summarization for context limits - [ ] Create memory persistence (save/load between games) --- #### 2.3 Chat History Summarization ⚑ - [ ] Implement automatic chat summarization - [ ] Configure separate smaller LLM for summarization (cost-effective) - [ ] Monitor chat history length (e.g., last 10 messages) - [ ] Trigger summarization when threshold reached - [ ] Create summarization prompt template - [ ] Build chat memory management - [ ] Keep only most recent message after summarization - [ ] Store summary in agent's memory - [ ] Maintain summary history for context - [ ] Add configuration for summarization settings - [ ] Summarization model selection - [ ] Message threshold for triggering - [ ] Summary format and length **Files to update:** - `pycatan/ai/memory.py` - Add chat summarization logic - `pycatan/ai/config.py` - Add summarization configuration - `pycatan/ai/llm_client.py` - Support multiple models (main + summarization) --- ### Phase 3: Core AI Agent πŸ€– **Goal:** Implement the main AI agent class #### 3.1 Base Agent Implementation - [ ] Create `AIAgent` class inheriting from `User` - [ ] Implement required User interface methods - [ ] `get_choice()` for decision-making - [ ] Other interaction methods as needed - [ ] Integrate with prompt manager - [ ] Integrate with memory system - [ ] Add agent state management **Files to create:** - `pycatan/players/ai_agent.py` - Main AI agent implementation (update existing stub) --- #### 3.2 LLM Integration - [ ] Create LLM client abstraction - [ ] Support for OpenAI API - [ ] Support for Anthropic Claude - [ ] Support for other providers (Azure, etc.) - [ ] Implement API call handling - [ ] Request formatting - [ ] Response parsing - [ ] Error handling and retries - [ ] Rate limiting - [ ] Add logging for all LLM interactions - [ ] Implement cost tracking **Files to create:** - `pycatan/ai/llm_client.py` - LLM API abstraction - `pycatan/ai/providers/` - Provider-specific implementations - `openai_provider.py` - `anthropic_provider.py` --- #### 3.3 Decision Pipeline - [ ] Build event-to-prompt conversion - [ ] Implement action extraction from responses - [ ] Create action validation before execution - [ ] Add decision logging and debugging - [ ] Implement decision timeout handling --- ### Phase 4: Monitoring & Debugging Infrastructure πŸ” **Goal:** Build essential tools for observing and debugging agent behavior **⚠️ CRITICAL: These tools are essential for development and must be built early!** --- #### 4.1 Web Dashboard for Real-Time Monitoring 🌐 **Priority: HIGH - Required before agent testing** - [ ] Design web dashboard UI - [ ] Multi-agent view (tabs or split screen per agent) - [ ] Live prompt display with syntax highlighting - [ ] Agent reasoning/thinking display - [ ] Action selection visualization - [ ] Chat window with all messages - [ ] Game state summary panel - [ ] Build backend API for dashboard - [ ] WebSocket connection for live updates - [ ] Endpoints for prompt/response history - [ ] Agent state endpoints - [ ] Chat history endpoint - [ ] Implement prompt logging and streaming - [ ] Capture all prompts sent to LLM - [ ] Capture all responses from LLM - [ ] Stream to dashboard in real-time - [ ] Format for readability - [ ] Build agent reasoning viewer - [ ] Display internal_thinking/reasoning - [ ] Show action selection process - [ ] Highlight tool usage - [ ] Show memory updates **Files to create:** - `pycatan/monitoring/` - NEW monitoring package - `dashboard_server.py` - Flask/FastAPI server for dashboard - `event_logger.py` - Captures and broadcasts events - `prompt_tracker.py` - Tracks all LLM interactions - `pycatan/monitoring/web/` - Dashboard frontend - `index.html` - Main dashboard page - `dashboard.js` - Dashboard functionality - `dashboard.css` - Dashboard styling --- #### 4.2 Local Documentation & Logging πŸ“ **Priority: HIGH - Required for debugging** - [ ] Design local documentation structure - [ ] One folder per game session - [ ] One file per agent with structured log - [ ] Timestamp-based organization - [ ] Implement per-agent documentation - [ ] Agent configuration snapshot - [ ] All prompts sent (formatted) - [ ] All responses received (formatted) - [ ] Decision timeline with reasoning - [ ] Memory state snapshots - [ ] Tool usage log - [ ] Errors and warnings - [ ] Build structured logging format - [ ] JSON-based for easy parsing - [ ] Markdown reports for human reading - [ ] Searchable and filterable - [ ] Add game session documentation - [ ] Game state at each turn - [ ] All chat messages with timestamps - [ ] Final game results and statistics **Files to create:** - `pycatan/monitoring/local_logger.py` - Local file logging - `pycatan/monitoring/session_recorder.py` - Game session recording - `pycatan/monitoring/report_generator.py` - Generate readable reports **Output structure:** ``` logs/ └── game_sessions/ └── 2026-01-03_15-30-45/ β”œβ”€β”€ game_summary.json β”œβ”€β”€ chat_log.txt β”œβ”€β”€ agent_blue/ β”‚ β”œβ”€β”€ config.json β”‚ β”œβ”€β”€ prompts.log β”‚ β”œβ”€β”€ decisions.log β”‚ └── memory_snapshots.json β”œβ”€β”€ agent_red/ β”‚ └── ... └── agent_white/ └── ... ``` --- #### 4.3 Chat Management System πŸ’¬ **Priority: HIGH - Core game feature** - [ ] Design chat system architecture - [ ] Centralized chat manager - [ ] Message routing between players - [ ] Chat history per game - [ ] Public vs private messages - [ ] Implement chat manager component - [ ] Message queue/buffer - [ ] Broadcast to all players - [ ] Direct messages between players - [ ] Integration with GameManager - [ ] Build chat observation interface - [ ] Real-time chat display in web dashboard - [ ] Chat log export - [ ] Filter by sender/time - [ ] Define chat protocol - [ ] Message format (sender, content, timestamp, type) - [ ] Chat commands (if any) - [ ] Trade negotiation messages **Files to create:** - `pycatan/management/chat_manager.py` - Central chat management - `pycatan/management/message.py` - Message data structure **Integration points:** - GameManager receives messages from players - ChatManager distributes to other players and dashboard - AI agents see messages in their prompt context - Web dashboard shows live chat - Local logs record all messages --- ### Phase 5: Tool System πŸ”§ **Goal:** Provide computational tools for agent decision-making #### 5.1 Core Tools - [ ] **Probability Calculator** - [ ] Dice roll probabilities for tiles - [ ] Expected resource generation rates - [ ] Statistical analysis helpers - [ ] **Resource Tracker** - [ ] Historical resource generation - [ ] Resource scarcity analysis - [ ] Production trend analysis - [ ] **Path Finder** - [ ] Optimal road placement - [ ] Longest road calculation - [ ] Connectivity analysis - [ ] **Trade Evaluator** - [ ] Fair trade assessment - [ ] Trade benefit calculation - [ ] Market value estimation **Files to create:** - `pycatan/ai/tools/` - Tool implementations - `probability_tool.py` - `resource_tool.py` - `pathfinding_tool.py` - `trade_tool.py` - `tool_manager.py` - Tool orchestration --- #### 5.2 Tool Integration - [ ] Define tool interface/protocol - [ ] Implement tool calling from prompts - [ ] Add tool usage limits per decision - [ ] Create tool result formatting - [ ] Build tool usage logging --- ### Phase 6: Testing & Validation βœ… **Goal:** Ensure agent works correctly and plays reasonably #### 6.1 Unit Tests - [ ] Test prompt manager filtering - [ ] Test response parser with various inputs - [ ] Test memory operations - [ ] Test each tool independently - [ ] Test configuration loading **Files to create:** - `tests/unit/test_ai_agent.py` - `tests/unit/test_prompt_manager.py` - `tests/unit/test_memory.py` - `tests/unit/test_tools.py` --- #### 6.2 Integration Tests - [ ] Test agent in complete game loop - [ ] Test agent vs human player - [ ] Test multiple AI agents playing together - [ ] Test edge cases and error scenarios - [ ] Test long-running games (memory management) **Files to create:** - `tests/integration/test_ai_gameplay.py` - `tests/integration/test_multi_agent.py` --- #### 6.3 Gameplay Validation - [ ] Verify legal moves only - [ ] Check strategic decision quality - [ ] Evaluate social interaction naturalness - [ ] Monitor LLM costs and performance - [ ] Collect agent behavior metrics --- ### Phase 7: Optimization & Enhancement πŸš€ **Goal:** Improve agent performance and capabilities #### 7.1 Performance Optimization - [ ] Reduce prompt token usage - [ ] Implement response caching for similar situations - [ ] Optimize tool execution - [ ] Improve decision speed --- #### 7.2 Strategy Enhancement - [ ] Tune agent personalities - [ ] Improve opening game strategy - [ ] Enhance mid-game adaptation - [ ] Refine end-game tactics - [ ] Better negotiation and trading --- #### 7.3 Advanced Features - [ ] Multi-turn planning capability - [ ] Opponent modeling - [ ] Meta-strategy learning - [ ] Tournament play support - [ ] Statistical performance tracking --- ## πŸ“ Project Structure (Proposed) ``` pycatan/ β”œβ”€β”€ ai/ # NEW: AI agent infrastructure β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ config.py # Configuration management β”‚ β”œβ”€β”€ prompt_manager.py # Prompt processing pipeline β”‚ β”œβ”€β”€ state_filter.py # Game state filtering β”‚ β”œβ”€β”€ prompt_templates.py # Prompt templates β”‚ β”œβ”€β”€ response_parser.py # Response parsing β”‚ β”œβ”€β”€ schemas.py # JSON schemas β”‚ β”œβ”€β”€ memory.py # Memory system + chat summarization β”‚ β”œβ”€β”€ llm_client.py # LLM abstraction (multi-model) β”‚ β”œβ”€β”€ providers/ # LLM provider implementations β”‚ β”‚ β”œβ”€β”€ __init__.py β”‚ β”‚ β”œβ”€β”€ openai_provider.py β”‚ β”‚ └── anthropic_provider.py β”‚ └── tools/ # Agent tools β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ tool_manager.py β”‚ β”œβ”€β”€ probability_tool.py β”‚ β”œβ”€β”€ resource_tool.py β”‚ β”œβ”€β”€ pathfinding_tool.py β”‚ └── trade_tool.py β”œβ”€β”€ monitoring/ # NEW: Monitoring & debugging β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ dashboard_server.py # Web dashboard backend β”‚ β”œβ”€β”€ event_logger.py # Event capture and broadcast β”‚ β”œβ”€β”€ prompt_tracker.py # LLM interaction tracking β”‚ β”œβ”€β”€ local_logger.py # Local file logging β”‚ β”œβ”€β”€ session_recorder.py # Game session recording β”‚ β”œβ”€β”€ report_generator.py # Report generation β”‚ └── web/ # Dashboard frontend β”‚ β”œβ”€β”€ index.html β”‚ β”œβ”€β”€ dashboard.js β”‚ └── dashboard.css β”œβ”€β”€ management/ β”‚ β”œβ”€β”€ actions.py # Existing β”‚ β”œβ”€β”€ game_manager.py # Existing β”‚ β”œβ”€β”€ log_events.py # Existing β”‚ β”œβ”€β”€ chat_manager.py # NEW: Chat management β”‚ └── message.py # NEW: Message data structure β”œβ”€β”€ players/ β”‚ β”œβ”€β”€ ai_agent.py # UPDATE: Full AI agent implementation β”‚ β”œβ”€β”€ human_user.py # Existing β”‚ └── user.py # Existing └── ... # Existing structure logs/ # NEW: Local documentation └── game_sessions/ └── YYYY-MM-DD_HH-MM-SS/ β”œβ”€β”€ game_summary.json β”œβ”€β”€ chat_log.txt └── agent_/ β”œβ”€β”€ config.json β”œβ”€β”€ prompts.log β”œβ”€β”€ decisions.log └── memory_snapshots.json examples/ β”œβ”€β”€ ai_testing/ β”‚ β”œβ”€β”€ config_example.yaml # NEW: Example configuration β”‚ β”œβ”€β”€ test_single_agent.py # NEW: Test script β”‚ └── test_multi_agent.py # NEW: Multi-agent test └── ... tests/ β”œβ”€β”€ unit/ β”‚ β”œβ”€β”€ test_ai_agent.py # NEW β”‚ β”œβ”€β”€ test_prompt_manager.py # NEW β”‚ β”œβ”€β”€ test_memory.py # NEW β”‚ └── test_tools.py # NEW β”œβ”€β”€ integration/ β”‚ β”œβ”€β”€ test_ai_gameplay.py # NEW β”‚ └── test_multi_agent.py # NEW └── ... ```