Play 93
Continual Learning Agent
Very High✅ Ready
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
Azure OpenAIAzure Cosmos DBAzure AI SearchAzure Redis CacheAzure Functions
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
Memory retention policyReflection trigger rulesKnowledge distillation frequencyLearning rate decaySession context window
Estimated Cost
Dev/Test
$100-250/mo
Production
$3K-10K/mo