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MLflow
@mlflow.org

Evaluating AI agents is tricky—they make multi-step decisions, use tools, and follow reasoning chains. To measure them, you need to assess the workflow, not just the final answer. That’s where MLflow’s new agent evaluation framework steps in. 🔗 Docs: mlflow.org/docs/latest/... #MLflow #AI #MLOps

MLflow
@mlflow.org

MLflow lets you create custom scorers for agent behavior: did it use the right tool, in the right order, with proper reasoning? Datasets can encode patterns + decisions, not just input–output. You’re testing how the agent thinks—not just what it outputs. #AgentEvaluation #MLflow #opensource

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MLflow
@mlflow.org

The real unlock → MLflow’s tracing integration. Every tool call + reasoning step gets captured and replayable. When an agent fails, you can see why—not guess. Critical for debugging multi-step chains + production bottlenecks. 🔗 Learn more: mlflow.org/docs/latest/... #LLMOps #AI #MLflow #oss

mlflow.orgEvaluating Agents | MLflowAI Agents are an emerging pattern of GenAI applications that can use tools, make decisions, and execute multi-step workflows. However, evaluating the performance of those complex agents is challenging...