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Experience & Autonomy

Maturity: Experimental

These components are experimental explorations of autonomy patterns. They implement the architectural scaffolding but have not been validated in production autonomous workflows. The learning layer (T0-T3 strategies) is the production-grade self-improvement mechanism.

Four components exploring experience loops and autonomous orchestration patterns.

Overview

Experience & Autonomy explores architectural patterns for transitioning pipelines from tool-assisted to increasingly autonomous operation. The experience_loop component provides a basic observe-update-refine cycle. autonomous_orchestrator manages multi-step workflow sequencing, while autonomy_governor enforces bounds on permitted autonomous actions.

human_development models progressive handoff from human-guided to autonomous operation, tracking confidence thresholds. For production self-improvement, use the Learning Layer (T0-T3 strategies) instead.

Components

Component Description MCP Tools
experience_loop Learn-from-experience execution cycle --
autonomous_orchestrator Multi-step autonomous workflow management --
autonomy_governor Autonomy boundary enforcement --
human_development Progressive human-to-autonomous handoff --

Architecture

graph TD
    EXP[experience_loop] --> AO[autonomous_orchestrator]
    AO --> AG[autonomy_governor]
    AO --> HD[human_development]
    EXP --> MEM[adapt_memory]
    AG --> CSF[Safety & Alignment]
    EXP --> AGENTS[Agents & LLM]
    AO -.->|cognitive strategies| COG[Cognitive Architectures]

Key Patterns

Experience Cycle. The experience loop follows a four-phase cycle: (1) select action using current policy, (2) execute via agent, (3) observe outcome and compute reward signal, (4) update policy. This is domain-agnostic -- the same loop works for code generation, data analysis, or physical tasks.

Autonomy Escalation. autonomy_governor defines escalation levels. New tasks start at the lowest autonomy level (human approval required for each step). As the system demonstrates competence (measured by align_evals metrics), human_development raises the autonomy threshold. The governor can revoke autonomy if failure rates increase.