Thanks for pointing out the elephant in the room with LLMs.
The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
My thesis isn't that we can stop the hallucinating (non-determinism), but that we can bound it.
If we wrap the generation in hard assertions (e.g., assert response.price > 0), we turn 'probability' into 'manageable software engineering.' The generation remains probabilistic, but the acceptance criteria becomes binary and deterministic.
Unfortunately, the use-case for AI is often where the acceptance criteria is not easily defined --- a matter of judgment. For example, "Does this patient have cancer?".
In cases where the criteria can be easily and clearly stipulated, AI often isn't really required.
My thesis is that even in those "fuzzy" workflows, the agent's process is full of small, deterministic sub-tasks that can and should be verified.
For example, before the AI even attempts to analyze the X-ray for cancer, it must: 1/ Verify it has the correct patient file (PatientIDVerifier). 2/ Verify the image is a chest X-ray and not a brain MRI (ModalityVerifier). 3/ Verify the date of the scan is within the relevant timeframe (DateVerifier).
These are "boring," deterministic checks. But a failure on any one of them makes the final "judgment" output completely useless.
steer isn't designed to automate the final, high-stakes judgment. It's designed to automate the pre-flight checklist, ensuring the agent has the correct, factually grounded information before it even begins the complex reasoning task. It's about reducing the "unforced errors" so the human expert can focus only on the truly hard part.