Play 07
Multi-Agent Service
High✅ Ready
Supervisor agent routes to specialist agents with shared state and handoff protocol.
A supervisor agent receives requests, classifies intent, and delegates to specialist agents (research, coding, data analysis, etc.). Each agent has its own model config, tools, and memory. Dapr provides state management and pub/sub messaging. Loop prevention and max-iteration guards keep costs predictable.
Architecture Pattern
Agent-to-agent handoff, supervisor pattern, shared state
Azure Services
Container AppsAzure OpenAI (gpt-4o, dual)Cosmos DBService BusDapr
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Multi-Agent Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (107 lines), evaluate (108 lines), tune (109 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with Azure OpenAI key + envFile
TuneKit (AI Config)
- config/openai.json — per-agent model params
- config/guardrails.json — loop prevention, max iterations
- config/agents.json — agent roles, delegation rules, fallback chains
- evaluation/test-set.jsonl — handoff scenarios
Tuning Parameters
Supervisor routing logicTool schemas per agentAgent memory scopeFallback chainsMax iterations (default 5)
Estimated Cost
Dev/Test
$150–350/mo
Production
$2K–7K/mo