Play 93
Continual Learning Agent
Agent that persists knowledge across sessions and starts smarter every time.
Agent that persists knowledge across sessions, reflects on failures, detects patterns in tool outcomes, and surfaces accumulated learnings so each session starts smarter. Implements memory hooks, reflection patterns, and knowledge distillation for ever-improving AI assistance.
Architecture Pattern
Continual learning loop: session capture - failure reflection - pattern detection - knowledge distillation - context priming
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Learning Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (225 lines), evaluate (120 lines), tune (237 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + AI Search inputs + envFile
TuneKit (AI Config)
- config/openai.json - reflection and distillation prompts
- config/memory.json - retention policies, reflection triggers, decay rates
- config/guardrails.json - memory size limits, knowledge quality thresholds
- evaluation/eval.py - Learning retention >90%, Session improvement >15%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.