Supervisor Agent

• Key points are in the visual diagram above.

One LLM can't specialize in everything. A supervisor provides the management layer that routes work to experts and maintains global context.

The supervisor is a project manager who doesn't write code but knows which engineer to assign, tracks deadlines, and merges pull requests into a shippable release.

Scenario

Supervisor receives 'Write competitive analysis' → delegates market data to Research Agent, comparison table to Analyst Agent, merges into final doc.

Solution

In Multi-Agent Systems, apply Supervisor Agent to this scenario: Supervisor receives 'Write competitive analysis' → delegates market data to Research Agent, comparison table to Analyst Agent, merges into final doc. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Supervisor Agent (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Supervisor Agent happens in the code.

1def supervisor(state):  # define a reusable function2    plan = llm.invoke(f"Decompose: {state['goal']}")3    return {"plan": plan, "next_worker": plan.steps[0].agent}  # return the result4
5graph.add_conditional_edges("supervisor", lambda s: s["next_worker"],  # key line for Supervisor Agent6    {"research": "research_worker", "analyst": "analyst_worker"})

• Treating Supervisor Agent as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for supervisor agent

Quick recap — the most important points from this module.