Self‐Evolving Agent Structures - AGI-Corporation/frontier-os-app-builder GitHub Wiki

Self-Evolving Agent Structures

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Overview

The defining feature of agent.bay is that every pipeline is not a static workflow — it is a self-evolving agent structure. This means pipelines continuously learn from execution data, routing logs, task outcomes, and environmental signals to improve their own behavior over time without requiring manual redeployment.

Self-evolution happens across three dimensions:

  1. Role-level adaptation — individual Observer, Architect, Auditor, or Planner agents tune prompts and decision logic.
  2. Pipeline-level adaptation — the composition and weighting of roles shifts based on task success patterns.
  3. Ecosystem-level adaptation — cross-pipeline signals (e.g., via NANDA) inform routing heuristics across the marketplace.

The Evolution Feedback Loop

   Task Dispatched
         ↓
   Pipeline Executes Role(s)
         ↓
   Routing Logs Captured (inbound + outbound)
         ↓
   Task Status Resolved (completed / failed)
         ↓
   Evolution Engine Reads History
         ↓
   Agent Policies Updated
         ↓
   syncPipeline() reflects new state
         ↓
   Next Task Benefits from Improvement

The loop is anchored by two data structures:

  • EvolutionTask.logs — embedded routing logs per task
  • EvolutionPipeline.taskCount + syncedAt — pipeline-level evolution signals

What Self-Evolving Agents Can Do

Current (Mock / Foundation)

Capability Status Notes
Capture routing logs per task ✅ Live TaskRoutingLog inbound + outbound
Track task history per pipeline ✅ Live taskCount + task store
Sync pipeline state ✅ Live syncPipeline() + syncedAt
In-memory evolution state ✅ Live Mock store, resets on restart

Planned (Production Evolution)

Capability Status Notes
Persistent evolution metadata 🚧 Planned DB-backed policy store
Prompt auto-tuning per role 🚧 Planned A/B test prompts across task batches
Role weight adjustment 🚧 Planned Shift observer/architect balance by outcome
Spawning child pipelines 🚧 Planned Fork pipelines for recurring sub-problems
Cross-pipeline signal sharing 🚧 Planned NANDA-powered ecosystem learning
Governance-approved upgrades 🚧 Planned DAO voting on major evolution changes
Evolution audit logs 🚧 Planned "How did this agent change over time?" view

Role-Level Self-Evolution

Each Evolution role evolves independently within its domain:

Observer 👁️

  • Learns which log patterns consistently precede failures.
  • Adjusts sampling frequency and event filters based on signal-to-noise ratios.
  • Builds a local taxonomy of observed system states.

Architect 🧠

  • Tracks which patch strategies yield completed vs failed tasks.
  • Weights code change approaches by historical success per codebase type.
  • Reduces token waste by learning problem-to-template mappings.

Auditor 🛡️

  • Refines safety thresholds based on audit outcomes.
  • Learns which constraint violations are recurring and adds preemptive checks.
  • Integrates feedback from rejected patches to harden future validation.

Planner 🚀

  • Improves backlog prioritization by tracking which features delivered value.
  • Learns team velocity and adjusts sprint planning heuristics.
  • Integrates GitManager history to avoid repeating failed branch strategies.

Self-Evolution and Bitrefill Funding

Self-evolving pipelines can use their economic data as an evolution signal:

  • High task volume at a given pricePerTask signals market demand → pipeline may auto-scale or spawn a child.
  • Prepaid balance depletion rate signals urgency → prioritization hints for Planner agents.
  • Failed tasks that drained FND are weighted more heavily in loss functions than low-cost failures.

This creates a market-aligned evolution loop where economic incentives directly shape agent behavior.

NANDA and Ecosystem-Level Evolution

The NANDA Cross-Agent Bridge enables pipelines to:

  • Delegate subtasks to external specialist agents when local capability confidence is low.
  • Receive outcome signals from external agents and fold them into local evolution state.
  • Contribute anonymized routing patterns to the broader NANDA network for community learning.

Over time, the NANDA bridge itself becomes a meta-evolving router that improves delegation heuristics across the entire Frontier OS agent ecosystem.

Design Principles

  1. Non-destructive evolution — Agent changes are additive and version-tracked; rollback is always possible.
  2. Explainability — Every evolution event is logged with a reason, outcome, and timestamp.
  3. Governance hooks — Major policy changes require community or owner approval before activation.
  4. Isolation — Evolution state is scoped per pipeline; cross-pipeline learning is opt-in.
  5. Economic alignment — Evolution is shaped by market signals, not just technical metrics.

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