Phase 4: Multi-Agent Systems · 60 min · Python · Anthropic SDK
Supervisor / Orchestrator Pattern
One agent to rule the workflow, N agents to do the work.
Hiring signal: The supervisor pattern is the production standard for enterprise agentic systems. Stripe, Airbnb, and most companies building AI workflows use it — it provides the control plane, audit trail, and failure handling that ad-hoc 'chain of agents' approaches lack. Any AI system design interview for a senior role expects you to know this pattern by name.
What you will learn
- Implement a supervisor agent that routes tasks to worker agents using structured dispatching
- Handle worker failures with retry, escalation, and fallback strategies
- Build an audit trail of all supervisor decisions for debugging and compliance
The Problem
Without a coordinator, multi-agent systems collapse into chaos. Each agent completes its task without knowing if downstream agents need its output in a specific format. When a worker fails, nothing handles the retry. There's no record of what was tried and why. The supervisor pattern solves all of this: one orchestrator agent acts as the control plane — routing tasks, handling failures, tracking state, and producing the audit trail.
The supervisor is the system's nervous system. Workers are its hands. The supervisor knows the goal; workers know how to execute one specific piece.
Supervisor Architecture
One supervisor, N workers. The architecture is simple — the discipline is in the details.
The supervisor's responsibilities:
- Receive the top-level task
- Plan the workflow (which workers, in what order)
- Dispatch to appropriate workers
- Receive and evaluate results
- Decide on next steps (proceed, retry, escalate, complete)
- Handle failures with explicit strategies
- Synthesize the final output
The worker's responsibilities:
- Accept one task with clear context
- Execute using its specialized tools
- Return a structured result with status, confidence, and any errors
- Nothing else — workers don't coordinate with each other
From the supervisor's perspective, each worker is a tool. The supervisor calls researcher(task, context) the same way it would call a database query — except the "database" is an LLM with a specialized system prompt.
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You've read the first 2 sections. The rest of this lesson covers Structured Dispatch Protocol, Failure Handling in Supervisor Systems, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.
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