Build a Multi-Agent Research Team · 35 min · Python · Anthropic Python SDK · Pydantic

The Planner Agent

The Planner is the one agent whose only job is deciding what the other 4 should do -- get its output structured wrong and every downstream agent inherits the confusion.

Hiring signal: Task decomposition into structured, assignable sub-tasks is the actual hard part of any multi-agent system -- more than any individual specialist's prompt quality.

What you will learn

Build an agent that decomposes a research question into structured sub-tasks
Assign sub-tasks to specific specialist roles using structured output
Understand why the Planner uses a stronger model than the specialists

Introduction

The state schema exists but every field is empty. Today's agent is the first one that actually runs: given a raw research question, it decides what the other 4 agents should each go do.

Structured task assignment, not a free-form plan

from pydantic import BaseModel
from typing import Literal

class TaskAssignment(BaseModel):
    specialist: Literal["web_researcher", "technical_analyst", "market_analyst"]
    sub_task: str

class TaskList(BaseModel):
    research_question: str
    tasks: list[TaskAssignment]

If you built Course 06's coding agent, this should feel familiar: a Pydantic schema, populated by prompting the model for JSON matching it, parsed and validated before anything downstream trusts it. The Literal["web_researcher", ...] type does real work here -- it constrains the model to only ever assign a sub-task to one of the 3 specialists that actually exist, rather than inventing a 4th role nothing in the graph knows how to route.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Why the Planner gets the stronger model, What You're Building — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Agent