Hollow

Phase 4: Agent Autonomy — Foundation Complete

Capabilities become executable. Agent intents drive autonomous decision-making.

Execution Engine

• Register capability implementations (Python callables)
• Execute capabilities with typed parameters
• Track execution history per agent with timestamps and duration
• Enable/disable capabilities at runtime
• Compute success rates and execution statistics
• Multi-agent isolation with separate execution histories

Reasoning Layer

• Agent submits intent → Reasoning Layer reasons about it → selects best capability
• Semantic capability discovery using embedding-space similarity
• Automatic parameter generation from intent (heuristic-based, future: LLM-based)
• Learning from execution outcomes (success/failure tracking)
• Confidence scoring for selected capabilities
• Multi-agent reasoning isolation

Integration

• Full end-to-end cycle: intent → reason → execute → learn
• Execution results automatically captured and stored
• Reasoning decisions logged with full causality trace
• Success rates tracked per agent for adaptive decision-making

Test Coverage

• 19 integration tests (11 execution + 8 reasoning), all passing
• Tests verify capability dispatch, parameter handling, error resilience, history tracking, multi-agent isolation, statistics
• Multi-agent tests ensure complete isolation of agent state

Current Capabilities

• Mock reasoning (heuristic parameter generation)
• Python function dispatch with timeout support
• Basic error handling and exception capture

Next Steps (v2.7.0)

• Autonomy Loop: goal pursuit, continuous execution, learning integration
• Synthesis integration: observe gaps, propose new capabilities
• Context persistence: agent state survives across sessions

Architecture: Pure embedding space. Semantic capability discovery, no JSON/REST symbolic translation.