---
id: agent-tool-use-evaluation
title: 'Agent Tool Use Evaluation'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-02'
created_date: '2026-06-02'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-agent-tool-use-evaluation-1
    statement: >-
      OpenAI agent evals documentation says agent evals can grade final outputs and traces.
    source_title: OpenAI Agent Evals
    source_url: https://platform.openai.com/docs/guides/agent-evals
    confidence: medium
  - id: fact-ai-agent-tool-use-evaluation-2
    statement: >-
      OpenAI trace grading documentation describes grading traces to evaluate intermediate steps in an agent run.
    source_title: OpenAI Trace Grading
    source_url: https://platform.openai.com/docs/guides/trace-grading
    confidence: medium
  - id: fact-ai-agent-tool-use-evaluation-3
    statement: >-
      LangSmith evaluation documentation describes multiple evaluation approaches, including reference-free, reference-based, and pairwise evaluation.
    source_title: LangSmith Evaluation Approaches
    source_url: https://docs.langchain.com/langsmith/evaluation-approaches
    confidence: medium
completeness: 0.83
known_gaps:
  - Tool-use evals need task-specific expected traces, environment fixtures, and assertions about side effects.
disputed_statements: []
primary_sources:
  - title: OpenAI Agent Evals
    type: documentation
    year: 2026
    url: https://platform.openai.com/docs/guides/agent-evals
    institution: OpenAI
  - title: OpenAI Trace Grading
    type: documentation
    year: 2026
    url: https://platform.openai.com/docs/guides/trace-grading
    institution: OpenAI
  - title: LangSmith Evaluation Approaches
    type: documentation
    year: 2026
    url: https://docs.langchain.com/langsmith/evaluation-approaches
    institution: LangChain
secondary_sources: []
updated: '2026-06-02'
ai_models:
  - gpt-5-codex
---

## TL;DR

Agent tool-use evaluation checks not only the final answer, but whether the agent chose the right tools, passed valid arguments, handled errors, and avoided unsafe side effects.

## Core Explanation

A tool-using agent can fail even when its final text sounds plausible. It might skip retrieval, call the wrong API, over-broaden permissions, retry a non-idempotent operation, or ignore a tool error.

Evaluation should inspect traces, tool arguments, returned artifacts, and final outputs together. Trace-level checks are especially useful for catching silent workflow regressions before production.

## Source-Mapped Facts

- OpenAI agent evals documentation says agent evals can grade final outputs and traces. ([source](https://platform.openai.com/docs/guides/agent-evals))
- OpenAI trace grading documentation describes grading traces to evaluate intermediate steps in an agent run. ([source](https://platform.openai.com/docs/guides/trace-grading))
- LangSmith evaluation documentation describes multiple evaluation approaches, including reference-free, reference-based, and pairwise evaluation. ([source](https://docs.langchain.com/langsmith/evaluation-approaches))

## Further Reading

- [OpenAI Agent Evals](https://platform.openai.com/docs/guides/agent-evals)
- [OpenAI Trace Grading](https://platform.openai.com/docs/guides/trace-grading)
- [LangSmith Evaluation Approaches](https://docs.langchain.com/langsmith/evaluation-approaches)