AgentOps Observability with Red Hat AI
Welcome to the AgentOps Observability with Red Hat AI workshop!
What you’ll learn
In this workshop, you will:
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Monitor the Stack: Utilize Red Hat AI’s out-of-the-box observability stack to track key metrics and logs of your Agentic App
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Trace Multi-Agent Executions: Track requests across multi-agent frameworks and MCP tools to understand the complete decision-making path
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LLM Evaluations: Combine tracing with LLM evaluations (Evals) in MLflow to ensure your agents maintain high-quality outputs
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From Development to Production: Move from manual notebook evaluations to automated AI pipelines that run continuously on the platform
Who this is for
This workshop is designed for SREs, Platform Engineers, and AI Developers/Engineers who want to implement end-to-end observability for multi-agent AI systems.
Workshop environment
You will work with a multi-agent mortgage lending application built with LangGraph and integrated with Model Context Protocol (MCP) tools.
Log into OpenShift with the following credentials:
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Username: user1
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Password: openshift
| All environment details and credentials are available in the lab interface. |