v5.1.3: Feedback Loop - Agent Learning Process
Feedback Loop Implementation
Transform agents from reactive executors into proactive learning systems!
What's New
Complete Feedback Loop Documentation (550+ lines):
- FEEDBACK-LOOP.md with 5-stage learning process
- Detection: Error and success triggers
- Analysis: Reflection prompts for systematic investigation
- Extraction: YAML templates with quality checklist
- Routing: Decision algorithm (Security Business Logic Universality)
- Commit: Project KB vs Shared KB submission
Real-World Examples (350+ lines):
- Docker healthcheck timeout
- Pydantic validation error
- Stripe webhook signature verification
- SQLAlchemy connection pool exhaustion
- Debug mode configuration
- Routing decision matrix
Enhanced PROJECT.yaml Template:
- agent_instructions section with feedback_loop config
- reflection_prompt with 4-step protocol
- search_first instruction
- Mandatory submission requirements
The 5-Stage Learning Process
- DETECTION - Error or success trigger
- ANALYSIS - Reflection: What why how
- EXTRACTION - Format as YAML
- ROUTING - Project KB vs Shared KB
- COMMIT - Submit via kb_submit.py
Benefits
For Agents:
- Never make the same mistake twice
- Build institutional memory
- Accelerate problem-solving
For Teams:
- Solutions preserved across sessions
- Faster onboarding
- Consistent problem-solving
For Organizations:
- Knowledge compounds over time
- New projects benefit instantly
- Reduces duplicate work
Quick Start
-
Initialize your project:
bash .kb/shared/tools/v5.1/init-kb.sh -
Review agent_instructions in .kb/context/PROJECT.yaml
-
Agent will automatically follow Feedback Loop when errors occur
-
Knowledge accumulates in Project KB and Shared KB
Documentation
Feedback Loop: https://github.com/ozand/shared-knowledge-base/blob/main/docs/v5.1/FEEDBACK-LOOP.md
Examples: https://github.com/ozand/shared-knowledge-base/blob/main/docs/v5.1/examples/feedback-loop-scenarios.md
Full Changelog: https://github.com/ozand/shared-knowledge-base/blob/main/CHANGELOG.md