Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent
In this tutorial, we build and execute a multi-agent workflow with Omnigent using a reliable, isolated Python environment created with uv. We configure a financial research lead agent that retrieves a live USD-to-EUR exchange rate from an external API, prepares a concise client-ready summary, and delegates its draft to a dedicated text-auditing sub-agent for clarity and length validation. We define reusable Python functions as callable agent tools, describe the complete agent structure in YAML,
Get smart on it
This tutorial demonstrates how to build a multi-agent workflow using Omnigent, a system that coordinates multiple AI agents to complete complex tasks. A financial research lead agent retrieves live currency exchange rates from an external API and delegates its draft summary to a text-auditing sub-agent for validation before providing a final answer. The setup uses Python functions as callable tools, defines agent behavior in YAML configuration files, and applies governance policies to limit tool calls and control costs. The workflow runs in an isolated Python environment without requiring interactive terminal access, showing how agents, tools, delegation, and policy controls can work together in a single system.
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