Building the Agent Graph | Multi Agent Workflows for Beginners | Part 9

Why It Matters

Mapping agents as a graph with conditional edges clarifies orchestration and decision-making in multi-agent systems, enabling scalable automation of complex tasks and easier debugging. This approach is practical for teams building coordinated agent workflows and accelerates deployment by separating roles and routing logic.

Summary

The presenter walks through constructing an agent graph for a multi-agent workflow, demonstrating how to define nodes (researcher, coder, supervisor), import required libraries, and instantiate a class to set up the workflow. They explain adding conditional edges that route decisions between nodes—essentially encoding logic for whether tasks go to the coder or researcher. The session covers compiling the graph and notes that an existing supervisor component need not be rerun. A notebook link and Q&A were promised for follow-up.

Original Description

In Part 9, we bring everything together and construct the agent graph that powers our workflow.

In this session:

Map out nodes (supervisor, researcher, coder) as logical units

Define edges and conditional routing between agents

Structure the workflow as a graph-based execution flow

Compile and initialize the graph for multi-agent coordination

This part focuses on turning independent components into a connected, decision-driven multi-agent system ready to process tasks and route intelligently.

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