So, the core is basically an audit loop on thinking tokens to re-steer the model?

Seems like an interesting direction to reduce token usage/agent error. This would be a lot easier to grok if you created a benchmark, that would both demonstrate your ideal use case and the potential improvement your method brings.

Also, I'd avoid directly using model output for HN/social media communications. I don't really care if the information is correct and concise, but some people get really triggered by it.

Close but not quite — it's not re-steering the model. AIP doesn't modify the agent's behavior or inject corrections back into the context. It's a passive observer that reads the thinking trace and produces a verdict. What happens after the verdict is up to the operator — you might block the action, escalate to a human, log it, or let it through with a flag.

Think of it less as a feedback loop and more as a conscience that watches but doesn't control. The agent doesn't know AIP is there (by design — if the agent knows it's being watched, you change the behavior you're trying to observe).

Benchmarks are actually coming shortly along with proofs. Noted on model outputs. FWIW, 90% of what I wrote here was me typing BUT using grammarly on my cleanup.

[EDIT] - overly specific answer. AIP doesn't re-steer, but our gateway implementation smoltbot does re-steer. That was our choice of how to implement AIP.