AI BriefingAgent Evaluation: A Detailed Guide
Key point
It summarizes how to design agent evaluation harnesses that go beyond static benchmarks.
Details
The focus of LLM evaluation has shifted from static benchmarks to agents that perform long-horizon tasks. Agents are not simply question-answering models; they are systems that interact with an environment based on reasoning, tool use, error recovery, and autonomy.
Agent systems are usually composed of three elements.
- LLM (or reasoning model): judges progress and chooses the next action.
- Tools: includes APIs, CLIs, MCP servers, and, if needed, computer-use primitives.
- Instructions: clearly defines the tools available and the expected behavior.
Tool calls can be naturally handled with special tokens and templates, but a well-designed tool should have clear documentation, non-overlapping roles, and easy recovery on failure. Whether a human can immediately use the tool just by reading the documentation is a key criterion for tool design.
Evaluation should also resemble real usage patterns. It emphasizes that rather than one-off accuracy rates, what matters is a realistic harness, long-horizon interaction, verification of intermediate results, error recovery, and building your own evaluations based on recent benchmark cases.
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