How to Test and Debug AI Agents (Complete Guide)

AI agents fail in ways traditional software does not. They hallucinate tool calls, misinterpret instructions, produce inconsistent output across runs, and silently drop steps in complex workflows. Testing and debugging AI agents requires different approaches than testing deterministic code.

Quick Answer: Testing AI agents requires three layers: unit tests for individual tool calls, workflow tests for multi-step processes, and evaluation tests for output quality. Debug agents by inspecting the reasoning trace, checking tool call inputs/outputs, and testing skills in isolation. LangSmith, Braintrust, and Promptfoo are the leading testing platforms. For skill-level testing, run against known inputs and compare outputs.

Traditional software either works or crashes. AI agents have a third state: they run successfully but produce wrong output. A code review agent that misses a security vulnerability, a sales agent that sends the wrong follow-up, or a documentation agent that generates inaccurate API references. Testing AI agents means catching these silent failures.

How do I test AI agent tool calls?

Mock the tool and verify the agent calls it with correct parameters. If your agent uses the GitHub MCP server to create pull requests, test that it sends the right branch name, title, and description. Tool call testing is the closest analog to unit testing in traditional software.

# Example: verify the agent calls GitHub correctly
result = agent.run("Create a PR for the login fix")
assert result.tool_calls[0].name == "create_pull_request"
assert "login" in result.tool_calls[0].args["title"].lower()

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How do I test AI agent workflows?

Run the complete workflow against known inputs and verify the output matches expected results. For a keyword-research skill, provide a known URL and verify the output contains the expected keyword categories, volume estimates, and recommendations. For a code review skill, provide code with known issues and verify they are flagged.

How do I test SKILL.md skills?

Install the skill, run it against 5 to 10 different inputs that cover the main use cases and edge cases, and compare outputs. Good skills produce consistent structure across runs even though the specific content varies. If the output format changes between runs, the skill instructions need to be more explicit.

Browse developer skills on Agensi to see well-structured skill examples.

How do I debug an AI agent that produces wrong output?

Three steps. First, inspect the reasoning trace. Most agent frameworks (LangChain, CrewAI) provide step-by-step logs showing the agent's reasoning at each decision point. Second, check tool call inputs and outputs. The agent might be calling the right tool with wrong parameters. Third, test the skill in isolation. Remove all other skills and test the problematic one alone to rule out conflicts between skills.

What testing platforms work with AI agents?

LangSmith provides tracing, evaluation datasets, and production monitoring for LangChain agents. Braintrust offers model evaluation with scoring rubrics and dataset management. Promptfoo handles prompt and skill testing with automated red-teaming. For simpler needs, a pytest suite that runs the agent against fixtures is effective.

How often should I test AI agent skills?

Test after every skill update, after upgrading the underlying model, and after adding new tools. Model updates frequently change agent behavior even when the skill instructions have not changed. A skill that worked with Claude 3.5 might behave differently with Claude 4. Run your test suite after every model upgrade.

Browse Testing & QA skills on Agensi.