Agents
Agent Trajectory Evaluation
Evaluating the sequence of steps an agent took, including its messages and tool calls, rather than only its final answer.
In depth
Evaluating the sequence of steps an agent took, including its messages and tool calls, rather than only its final answer. A trajectory can be matched against a reference in strict order, in any order, or as a subset or superset of the expected tool calls, or it can be graded by an LLM judge without a reference. It catches agents that reach a plausible answer through wrong, wasteful, or unsafe actions.
Primary source: github.com
Example
In practice, developers reach for Agent Trajectory Evaluation when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Agent Trajectory Evaluation?
Evaluating the sequence of steps an agent took, including its messages and tool calls, rather than only its final answer.
Why does Agent Trajectory Evaluation matter for AI developers?
Agent Trajectory Evaluation sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Agent Trajectory Evaluation?
Developers Digest publishes tutorials and videos that cover Agents topics including Agent Trajectory Evaluation. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
Agent Framework
A library that supplies the standard building blocks of an agent, such as calling LLMs, defining and parsing tools, chaining calls, and managing state, so you do not write them from scratch.
Agents
Agent Swarm
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.
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