Hollow

Phase 4: Agent Autonomy — Goal Pursuit Engine

Agents pursue goals indefinitely with continuous learning and adaptation.

Core Autonomy Loop

• execute_step: Single iteration of goal pursuit

  • Retrieve active goal for agent
  • Reason about next step to progress goal
  • Execute selected capability
  • Learn from execution outcome
  • Update goal progress metrics
  • Record step in execution chain

• pursue_goal: Multi-step goal pursuit

  • Loop execute_step until goal complete (progress >= 1.0) or max_steps reached
  • Automatic goal completion when progress metric reaches 1.0
  • Graceful handling of failed steps with backoff

Integration with Phase 3 + v2.6.0

• Goals: PersistentGoalEngine (Phase 3)

  • Long-term objectives persist across sessions
  • Semantic objective descriptions
  • Priority-based goal ordering
  • Progress tracking via metrics dict

• Reasoning: ReasoningLayer (v2.6.0)

  • Intent generation from goal description
  • Semantic capability discovery
  • Parameter generation
  • Learning from outcomes

• Execution: ExecutionEngine (v2.6.0)

  • Capability dispatch with parameters
  • Result capture and error handling
  • Execution history per agent

• Learning: SemanticMemory (Phase 3)

  • Execution results stored as embeddings
  • Available for future reasoning context
  • Agent experience accumulation

• Synthesis: CapabilitySynthesis (Phase 3)

  • Gap detection when no capability matches
  • New capability proposal
  • Quorum-based approval

Key Features

• Multi-agent isolation: Each agent has separate goals, execution chain, learning
• Progress tracking: Incremental progress (0.1 per successful step)
• Semantic matching: Goal objectives matched with capabilities by meaning
• Graceful degradation: Continues despite failures, learns from them
• Full causality: Every step linked to goal → reasoning → execution → outcome

Test Coverage

• 9 integration tests, all passing
• Single step execution with goal recording
• Multi-step goal pursuit with max_steps limits
• Execution chain tracking with full linking
• Success rate computation
• Multi-agent isolation verification
• Learning integration

What This Enables

Agents that:

• Set long-term goals once and pursue them indefinitely
• Reason about each step autonomously
• Learn from every execution outcome
• Adapt strategy based on success rates
• Request new capabilities when gaps detected
• Maintain coherent state across sessions
• Operate in pure embedding space (semantic, not symbolic)

Architecture

All systems work in embedding space:

• Goals: semantic objectives, similarity search
• Capabilities: semantic descriptions, cosine similarity discovery
• Learning: execution outcomes as 768-dim embeddings
• Reasoning: embedding-space intent → capability matching

No translation layers. No JSON. No symbolic manipulation. Pure agent-space.

Progression

• v2.6.0: Execution + Reasoning (19 tests)
• v2.7.0: Autonomy Loop (9 tests, total 28)
• v2.8.0: Self-Modification (full autonomous self-extension)
• v2.9.0: Swarm Coordination (multi-agent mesh)
• v3.0.0: Complete Autonomous Agent

Total Phase 4 Progress: 28/54 integration tests (52%)