How to Trace and Debug AI Coding Agents with Omnigent and MLflow

Managing multi-agent workflows across different coding harnesses can lead to fragmented context, lost guardrails, and zero visibility. In this video, discover how to solve these problems by integrating the open-source meta-harness, Omnigent, with MLflow. Learn how Omnigent unifies context and policies across diverse coding tools while delegating tasks to agents like CodeX and Claude. See step-by-step how to launch an OpenTelemetry environment, run a local multi-agent workflow, and use MLflow Traces to track exact tool calls, measure latency, and monitor token usage. What you will learn: 🔹 The challenges of switching between multiple AI coding harnesses. 🔹 How Omnigent provides a unified interface and shared context across agent boundaries. 🔹 Setting up your environment and tracking live multi-agent execution. 🔹 Using the MLflow UI to analyze traces, inspect tool chains, and debug failures. Links & Resources: 🔗 MLflow Documentation: https://mlflow.org/docs/latest/index.html 🔗 Omnigent Repo: https://github.com/databricks/omnigent 🔗 MLflow LLM Tracking & Tracing Guide: https://mlflow.org/docs/latest/llms/tracing/index.html