Introducing AgentProbe: A Testing Framework for AI Agents
The article introduces AgentProbe, a testing framework for AI agents that helps developers test the behavior of their AI agents, including tool selection, decision-making, error handling, and sensitive data processing.
Why it matters
As AI agents become more prevalent in production systems, it's critical to have a robust testing framework to ensure their reliable and secure behavior.
Key Points
- 1Existing testing tools don't cover the unique challenges of AI agent behavior
- 2AgentProbe brings the same test-driven discipline used for web apps to AI agents
- 3AgentProbe supports chaos testing, contract testing, multi-agent testing, and record & replay
- 4The framework is battle-tested, with over 2,900 passing tests
Details
The article highlights the problem that many AI agents are running in production with zero tests, despite the fact that they call external tools, make autonomous decisions, handle errors, and process sensitive data. Existing testing tools like PromptFoo and DeepEval focus on prompts and outputs, but don't test the agent's behavior between receiving a request and returning a response. AgentProbe aims to address this gap by providing a testing framework for AI agents, allowing developers to define tests in YAML and run them in CI to get deterministic results. The framework supports features like chaos testing (injecting tool failures, slow responses, and malformed outputs), contract testing (verifying that tool calls match expected schemas), multi-agent testing (testing pipelines where multiple agents collaborate), and record & replay (recording live agent sessions for regression testing). AgentProbe is a battle-tested framework, running over 2,900 passing tests against itself.
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