AI Engineering Now #003: Your AI Agent Needs a Test Before More Tools — Why Agent Evaluation Matters

AI Engineering Now #003

11 November 2026 | AI Agent Evaluation

Your AI Agent Needs a Test Before It Needs More Tools

NOW

AI agents are becoming increasingly capable of using tools, retrieving information, writing code, and executing multi-step workflows.

But an agent successfully calling a tool does not mean it has successfully completed the task.

OpenAI's Agent Evals guidance explains how developers can evaluate agent performance using traces, graders, and repeatable evaluation datasets.

WHY IT MATTERS

An agent may execute every tool call correctly and still deliver the wrong result.

For AI engineers, the important question is not:

Did the agent use its tools?

It is:

Did the agent accomplish the intended task correctly?

Reliable AI Engineering requires evaluating outcomes, not just execution activity.

TRY THIS

Create a small evaluation dataset containing 10 realistic tasks for your AI agent.

For each task, record:

1. Expected result: What should the agent accomplish?

2. Actual result: What did it produce?

3. Tool errors: Did any tool calls fail?

4. Task success: Were the requirements satisfied?

Run the same 10 tasks after changing your model, prompts, or tools.

Compare the results before adding more capabilities.

ENGINEERING TAKEAWAY

Don't measure how busy your agent is. Measure how well it accomplishes the task.

SOURCE — OpenAI Developer Documentation

Agent Evals

https://developers.openai.com/api/docs/guides/agent-evals

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