Testing and Debugging Agents

Learning Objectives Test AI agents effectively despite their nondeterministic nature Recognize and debug the most common agent failure modes Build monitoring and observability into your agents from the start Why Agent Testing Is Hard Traditional software is deterministic: given the same input, you get the same output. If a function returns the wrong value, you can reproduce the bug reliably. Agents are nondeterministic. The same input can produce different outputs, different tool call sequences, and different results every time. The model might choose to search the web first on one run and read a file first on the next. Both paths might be valid — or one might lead to a subtle error. This makes traditional testing approaches insufficient. You need new strategies. The Three Levels of Agent Testing Level 1: Component Testing (Test the Parts) Before testing the agent as a whole, test each component independently: Tool tests: Does each tool work correctly with valid input? With edge cases? With invalid input? These are standard unit tests — deterministic and reliable. Prompt tests: Does the system prompt produce reasonable responses for representative inputs? Run 10 to 20 diverse inputs through the model (without tools) and check that the reasoning is sound. This catches prompt issues early. Integration tests: Does each tool integration work end-to-end? Can the agent actually call the API, read the file, or query the database? Level 2: Behavioral Testing (Test the Agent) Test the agent's end-to-end behavior on representative tasks: Golden test cases: Create 10 to…