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Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent

MarkTechPostJuly 31, 20264 min read

In this tutorial, we demonstrate how to build and execute a multi-agent workflow with Omnigent in a secure, isolated Python environment. Learn to integrate live exchange-rate data, implement hierarchical agent delegation for financial text auditing, and apply hard governance policies—such as cost budgets and tool call limits—to your research pipeline directly from Google Colab. The post Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent appeared first on MarkTechPost.

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…

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