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MULTMOD.MULT-08 · v1.0

Agents that
collaborate,
not compete.

8 micro-lessons · ~78 min · Real Docker images

THE TEAM MIXER · LIVE
MIX 04CH · -0.6dB
COLLAB
CRITIC
-1.2dB
PLANNER
5.3dB
EXECUTOR
1.9dB
OBSERVER
-3.8dB
MASTER OUT52% · cohesive
MULTAI ENGINEERINGTRENDING

Multi-agent systems

Modular agents that collaborate — without the 17.2× error trap.

WHY THIS MATTERS · MAST · NEURIPS 2025 · LINUX FOUNDATION AGENTIC AI FOUNDATION
MCP and A2A are now Linux Foundation standards (Dec 2025 / Jun 2025). The MAST taxonomy quantified that unstructured 'bag of agents' designs amplify errors 17.2× vs single-agent — and named the 14 failure modes engineers must defend.
WHAT YOU'LL LEARN
01When (and when NOT) to split into many agents
02The 7 canonical coordination patterns
03State, memory & blackboards
04MCP & A2A — communication contracts
05LangGraph 1.0 supervisor team
06OpenAI Agents SDK hand-offs
07Team-level evaluation
08Production hardening
YOU'LL BE ABLE TO
Decompose a problem across N agents and pick the right coordination pattern
Ship a LangGraph 1.0 supervisor team with checkpoints and Langfuse traces
Defend the 14 MAST failure modes (deadlock, role drift, prompt-injection cascade...)
SKILLS YOU'LL GAIN

Real skills, real career delta.

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.

RUNNABLE ON YOUR MACHINE
$ docker pull snap/multi-agent:hello
$ docker run --rm -it snap/multi-agent:hello
snap/multi-agent:hello
QUICK PREVIEW · 7 MIN
VERIFIED ENGINEER REVIEWS
The pattern-zoo container is the spike kit I wish I'd had a year ago.
@multi_agent_anaVERIFY ON GITHUB
Worth it just for the team-eval-harness. We use it as a CI gate now.
@devops_julesVERIFY ON GITHUB
LESSONS8
HOURS~1.3
LEARNERS2,317
THIS WEEK+38%