This roadmap documents the evolution of the Axiom Agent, from its foundational architecture to its future as a sophisticated, autonomous reasoner. It's organized by phases of evolution. Each phase represents a significant leap in capability and includes goals, deliverables, and key features.

The Axiom Agent has successfully evolved through its foundational phases. It began with the "Genesis Engine," establishing a stable knowledge graph. The "Intelligent Learner" phase enabled autonomous discovery, while the "Conversational Mind" gave it the ability to parse complex sentences and learn from its own responses.

Following this rapid innovation, the Axiom Agent has now completed a critical "Stabilization & Hardening" phase. This engineering-focused effort addressed technical debt, dramatically improved the user experience, and fortified the agent's core logic with a comprehensive test suite and more intelligent learning mechanisms. This essential work has created a robust and maintainable platform, paving the way for the agent's next major functional leaps: achieving true "Semantic Mastery" with a deeper understanding of language, becoming an "Autonomous Scholar" with strategic learning, and finally, evolving into a "Logical Reasoner" capable of procedural thought and tool use.

• Treat each phase as a major strategic goal.
• The features within each phase can be broken down into discrete tickets/issues.
• For each completed phase, its summary serves as a record of the agent's growth.

Goal: Establish the core symbolic-first architecture and knowledge representation.

• ConceptGraph implemented using NetworkX for in-memory knowledge storage.
• Basic SymbolicParser for simple sentence structures.
• Initial brain seeding process to provide foundational knowledge.
• Core CognitiveAgent class to orchestrate the basic chat loop.

Goal: Enable autonomous, self-directed learning and knowledge expansion.

• KnowledgeHarvester implementation for autonomous study and discovery cycles.
• CycleManager to switch between learning and refinement phases.
• Integration with WordNet to enrich the agent's vocabulary and conceptual understanding.
• Curiosity mechanism to generate follow-up questions for study.

Goal: Enhance conversational fluency and the ability to learn from interaction.

• UniversalInterpreter integration, using an LLM for complex language understanding.
• Coreference resolution to handle pronouns and maintain conversational context.
• Introspection loop allowing the agent to parse its own synthesized responses to learn new facts.
• Conflict detection and clarification mechanism for exclusive relationships.

Goal: Solidify the foundation, improve user experience, and make the agent more robust and resilient.

• Intelligent Learning Pipeline: Implemented a sentence sanitizer at the core of the agent's cognitive loop, preventing it from learning "garbage facts" from poorly structured external data.
• Robust Parser Fallback: Implemented a "sanity check" to intelligently fall back to the LLM interpreter when the symbolic parser fails on complex or nonsensical input.
• Enhanced Self-Awareness: Created specific meta- intents and handlers for questions about the agent itself ("who are you?", "what is your purpose?").
• Conversational Resilience: Integrated fuzzy matching (thefuzz) to handle user typos and minor variations in entity names.
• Comprehensive Test Suite: Achieved high test coverage (>70%) and a fully passing CI/CD pipeline (Ruff, MyPy, Pytest), ensuring all core functionality is verified.
• Streamlined User Experience:
  • Replaced verbose startup logging with clean tqdm progress bars.
  • Automated LLM model downloading via a simple axiom-llm command.
  • Refactored developer scripts into professional command-line entry points (e.g., axiom-train, axiom-render).
  • Implemented versioned model rendering to keep the project directory clean.

Goal: Deepen the agent's understanding of language nuance, context, and complex relationships.

• Advanced Relationship Extraction: Enhance the KnowledgeHarvester and UniversalInterpreter to extract and represent multi-part relationships (e.g., (person, born_in, city, on_date, date)).
• Sentiment and Tone Analysis: Implement a mechanism to track the sentiment of the user's input and adjust the tone of synthesized responses accordingly.
• Knowledge Provenance: Add metadata to all learned facts, tracking their source (e.g., Wikipedia, user input), timestamp, and confidence score. This is a prerequisite for belief revision.
• Belief Revision System: Implement a strategy for resolving conflicting facts based on source reliability, confidence, and recency.

Goal: Transform the agent from a passive learner into a strategic researcher with long-term goals.

• Goal-Oriented Learning: Introduce a GoalManager that allows the agent to be given high-level learning objectives (e.g., "Become an expert on ancient Rome").
• Strategic Study Plans: The KnowledgeHarvester will use its learning goals to generate and execute multi-step study plans (e.g., find a Wikipedia page, read it, identify key entities, generate curious questions for each entity, then research those questions).
• Dynamic Knowledge Pruning: Implement a "forgetting" mechanism where facts that are consistently low-activation and low-confidence are periodically removed to keep the graph relevant.

Goal: Evolve beyond semantic knowledge to incorporate procedural and logical reasoning by introducing "Tools."

• Tool Use Framework: Create a ToolManager and a formal "Tool" interface that the agent can use. Each tool will be a specialized function for tasks the knowledge graph cannot perform.
• Mathematical Capability (First Tool):
  • Deliverable: A MathTool that integrates a symbolic math library like SymPy.
  • Logic: The SymbolicParser and UniversalInterpreter will be trained to recognize a new question_math intent.
  • Flow: When this intent is detected, _process_intent will route the mathematical expression to the MathTool for execution, and the result will be returned to the user.
• Real-Time Data (Second Tool):
  • Deliverable: A WebSearchTool (e.g., using a Google/DuckDuckGo API) and a CurrentDateTool.
  • Logic: The agent will be taught to recognize questions it cannot answer from its static knowledge (e.g., "What is the weather in New York?", "Who won the game last night?").
  • Flow: The agent will learn to route these questions to the appropriate tool to fetch live data from the internet.
• Code Execution (Advanced Tool):
  • Deliverable: A sandboxed PythonInterpreterTool that can execute simple Python code to answer complex procedural questions.
  • Flow: For questions like "What are the first 10 prime numbers?", the agent will learn to write and execute a small script instead of trying to find the answer in its knowledge graph.

Goal: Prepare the agent for massive scalability and collaborative learning.

• Pluggable Graph Backends: Refactor ConceptGraph to be an interface, with the current in-memory NetworkX implementation as the default and a new implementation for a graph database (e.g., Neo4j, RedisGraph) as an option for large-scale deployments.
• Agent Federation: Design a protocol that allows multiple Axiom Agents to query each other's knowledge, share facts, and collaborate on learning goals.