Evaluating AI Agents

A practical guide to grading an agent's path, not just its answer: tools and MCP, the trajectory, multi-turn conversation, trace analysis, and safety when the agent can act.

An agent fails while reporting success, in a fluent message that looks exactly like the ones it sends when it is right. The failure lives in the path the final answer throws away. This guide is the method for grading that path, from the first tool-correctness run to a traced, red-teamed, monitored pipeline.

Read our in-depth guide to:

• Read an agent as a trajectory, and make the trace the unit of evaluation
• Measure tools and MCP calls: right tool, right arguments, right order
• Grade the trajectory and the conversation, from loops to memory to role
• Build scenario datasets with expected tools, personas, and guardrail rows
• Analyze traces for the bug and the bottleneck: latency, tokens, cost
• Red team an agent that can act, and run it all on a schedule and a CI gate