Skills you'll gain
10- Diagnose when single-agent is failingWorking
Use the 3-signal test (tool count, cost/latency split, trust boundaries) plus measured eval lift to decide IF multi-agent is worth the cost — before writing any code.
- Pick a coordination pattern from 7 canonical optionsProduction
Router, supervisor, hierarchy, pipeline, hand-off/swarm, debate, blackboard, graph — recognise each, know the trade-offs, ship the right one.
- Build a LangGraph 1.0 supervisor teamProduction
Typed StateGraph, conditional edges, durable checkpoints, langgraph-supervisor library, Langfuse integration end-to-end.
- Implement OpenAI Agents SDK hand-offsProduction
Triage agent → specialist agents using the Agents SDK hand-off primitive with guardrails, sandboxes, and tracing on by default.
- Ship a Redis-backed blackboard patternWorking
Pydantic schemas + Redis scratchpad with race-condition tests; safe parallel fan-out + fan-in.
- Wire MCP and A2A for cross-stack interopWorking
MCP server for tool sharing, A2A signed Agent Cards for cross-framework agent calls — pair them like REST + JWT.
- Write team-level evals (MASEval / Braintrust)Production
Golden traces, regression suite gating CI, metrics: context-reuse rate, contradictory-output rate, decision-sync time, p95 latency.
- Defend against multi-agent failure modesAdvanced
Detect & prevent the 14 MAST modes: deadlock, infinite loops, role drift, prompt-injection cascade, recursion explosion.
- Per-agent observability + cost attributionProduction
Tag every LLM call with (trace_id, agent_id, parent_agent_id, tool); Langfuse / Phoenix / Weave dashboards over per-agent spend.
- Run a fully air-gapped multi-agent stackAdvanced
gpt-oss-20b via Ollama + smolagents + Letta + Redis + Prom/Grafana — the deployment regulated industries actually buy.