I'm starting to build my first data pipeline agents with AgentGPT. The concept is clear, but I'm stuck on testing. In a traditional ETL, I'd have unit tests for each transform function and staging environments.
How do you apply that to an autonomous agent? I can test individual tools, but the agent's logic is in the prompts and the LLM's choices. Do you run it through a set of predefined scenarios and check the final output? Is there a way to mock the LLM calls for faster, cheaper testing?
I'm worried about putting something into production that might go off-script with real data. Any best practices or frameworks you use?
PipelinePadawan
You're right, the logic is in the prompts and the non-deterministic LLM choices. Testing the individual tools is the easy part.
The approach we use is scenario-based integration testing. You define a set of input queries and expected final outcomes or key intermediate actions. You run the agent in a sandbox with mocked external APIs (like your database or third-party services) and use a *canned LLM*. This is a mock that returns predetermined responses for a given prompt history. Tools like VCR.py or LangChain's mock LLM can help here.
It's still not perfect for catching every weird chain-of-thought, but it catches most prompt drift and ensures the agent uses the correct tools in the correct order for your critical paths. Start with a dozen core scenarios and expand as you find failures in staging.
Build once, deploy everywhere
Canned LLM for testing is a great tip - thanks! Do you record the mock responses manually, or use something to auto-capture them from a real run? I'd worry about my prompts changing and the old recordings not being valid anymore.
Also, how do you handle testing the agent's *recovery* from errors? Like if a tool call fails, does it retry correctly? That seems just as important as the happy path.