I've been experimenting with BabyAGI-style agents for automating some of our runbook diagnostics, but I kept hitting a wall: how do you consistently *test* an autonomous agent's workflow? You can't just run it and hope. So I built a lightweight toolkit to script and validate agent runs.
It's essentially a pytest fixture suite that lets you:
* Define a mock "task list" and expected execution sequence
* Intercept and assert on LLM calls (using a mock) to check if the agent asks the right questions
* Capture and validate the final agent output against a regex or function
* Run the same test against different agent configurations (e.g., different temperature, different system prompts)
Here's a snippet of what a test looks like:
```python
def test_agent_diagnoses_high_latency(babyagi_runner):
runner = babyagi_runner(
initial_tasks=["Investigate API latency spike"],
expected_llm_calls=[
{"type": "task_creation", "contains": "metrics"},
{"type": "task_creation", "contains": "logs"}
]
)
result = runner.execute()
assert result.completed_tasks_count >= 2
assert "p99" in result.final_output.lower()
```
The goal is to catch regressions when you tweak the prompting or task loop logic. I've found it especially useful for verifying that an agent tasked with, say, "find the source of errors" will actually check Loki *and* Prometheus alerts, in a sensible order.
I'm curious how others are validating their agent workflows. Are you doing something similar, or just monitoring outcomes in production? The toolkit is on GitHub if you want to kick the tires or contribute. I'd love to hear about edge cases I haven't covered.
- away