Planner Agent

Complex tasks fail when agents improvise step-by-step. Explicit planning upfront reduces wasted tool calls and makes progress auditable.

A planner is a GPS route calculator — it maps the full trip before you start driving, including which legs can run in parallel.

Scenario

Goal: 'Onboard new vendor Acme Corp' → Planner outputs 6 steps: legal review, create vendor record, setup payment, notify procurement, schedule kickoff.

Solution

In Multi-Agent Systems, apply Planner Agent to this scenario: Goal: 'Onboard new vendor Acme Corp' → Planner outputs 6 steps: legal review, create vendor record, setup payment, notify procurement, schedule kickoff. 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 Planner 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 Planner Agent happens in the code.

1class Plan(BaseModel):  # define a data structure or component2    steps: list[Step]3
4planner = Agent(output_type=Plan, instructions="Break goals into ordered steps with dependencies.")  # key line for Planner Agent5plan = planner.run_sync("Launch marketing campaign for Q3").output  # key line for Planner Agent

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

Quick recap — the most important points from this module.