In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the executor agent to execute those steps using reasoning or Python tool execution, and the aggregator agent to synthesize results into a coherent final response. By integrating tool usage, structured planning, and iterative execution, we create a fully autonomous agent system that demonstrates how modern AI agents reason, plan, and act in a scalable and modular manner.
