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AI Agents

Test an Agent Workflow Before You Trust It

Build scenario tests for normal, incomplete, adversarial, and tool-failure inputs so an agent has measurable behavior.

Intermediate18 minBy ToolDix Editorial

Learning objectives

  • Define agent scenarios beyond the happy path
  • Test tool failures and approval boundaries
  • Record traces that support debugging and review

ToolDix original visual

AI Agents practice loop
1

Frame

Name the outcome and constraints.

2

Build

Try one bounded workflow.

3

Review

Keep evidence, revise, and share.

Test behavior, not just final prose

An agent can produce a polished final answer while calling the wrong tool, skipping a required check, or inventing a source. A useful test therefore checks the path as well as the output. Capture the task, tool calls, retrieved evidence, decisions, and final response.

Use four scenario families

Start with a normal request. Add an incomplete request that requires clarification, a hostile request that attempts to override the task, and a tool failure such as a timeout or empty result. For each, define the expected action: proceed, ask, refuse, retry within a limit, or hand off.

Practice: write five acceptance cases

For one agent, write five cases with input, available tools, expected tool calls, expected boundary behavior, and a review criterion. Run them whenever you change the prompt, tools, or model. Keep failed traces because they are the fastest route to a stronger design.

Common mistake

Avoid using production data to prove an early agent works. A small synthetic fixture set gives you repeatable tests without exposing private information or making results impossible to compare.

Sources and license context

These references informed the lesson. ToolDix adds its own explanation, workflow, and practice rather than reproducing source material.

Take it further

Use a primary source to deepen this lesson.

Each recommendation is a direct link to the publisher or author. The study prompt is ToolDix editorial guidance, not copied course content.

LLM Course by Hugging Face

Hands-on lab

LLM Course

Complete the pipeline exercise, then write down what information disappears when text becomes tokens.

Open original source
Attention Is All You Need by arXiv

Classic reading

Attention Is All You Need

Read the abstract and architecture figure first; annotate what information flows between tokens.

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AI Agents Course by Hugging Face

Course

AI Agents Course

Before using a framework, write down one tool contract and the exact state an agent is allowed to change.

Open original source