Play 21
Agentic RAG
High✅ Ready
Autonomous retrieval — the agent decides when, what, and where to search.
Autonomous RAG where the AI agent controls retrieval end-to-end. Unlike standard RAG (fixed pipeline: query → search → generate), the agent decides when to search, what to search for, which sources to query (AI Search, Bing, SQL, custom APIs), iterates on results if insufficient, and synthesizes across multiple knowledge bases with proper citations. Self-evaluates response quality (groundedness ≥0.95) before returning. Semantic caching reduces repeat query costs by >60%.
Architecture Pattern
Agentic RAG: agent-controlled retrieval, multi-source, semantic caching
Azure Services
Azure OpenAI (gpt-4o)Azure AI SearchContainer AppsKey Vault
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Agentic RAG Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (110 lines), evaluate (104 lines), tune (104 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 — temp=0.1, seed=42, structured output
- config/search.json — multi-source ranking, semantic cache TTL
- config/guardrails.json — groundedness ≥0.95, abstention on low confidence
- evaluation/eval.py — Groundedness >0.95, Coherence >0.90, Relevance >0.90
Tuning Parameters
Retrieval strategy (depth-first vs breadth)Source ranking weightsIteration depth (1→5)Citation formatSemantic cache TTLToken budgets per source
Estimated Cost
Dev/Test
$150–300/mo
Production
$2K–6K/mo