Most policy boundaries
aren't enforceable.
You decide what your agent must never do, write the policy, and it passes every check you have. Then it fails in production, or worse, it starts blocking real customers. We tell you which of your boundaries actually hold, measured against your own traffic, in days rather than a quarter.
The failure modes pass every naive check.
These are all real, all measured, and none of them showed up in the obvious tests:
A support agent scoped to one product looked flawless, until we deleted the product's name from the test questions. It had learned the brand, not the domain. Rephrased the way a customer actually writes, it refused its own users 94% of the time.
A policy passed its safety invariant perfectly, zero legitimate requests blocked, while quietly pushing nine in ten real customer questions into a confirmation step. The check we were running could not see it.
And one boundary turned out not to be expressible at all. For a bank, "I'm getting an error code when I try to make a transfer" scores more like a coding request than "write me a SQL query" does. No threshold separates them, so "don't be a free coding assistant" cannot be enforced in that domain, by us or anyone else.
That last one took seconds to discover. The alternative was finding out after a fine-tuning run and a week of blocking customers whose payments had failed.
What we actually do.
Policy viability assessment. Send us a sample of your real traffic and the boundaries you want to enforce. We come back with which are expressible, which are not, the calibrated cost of each in false-confirms and coverage, and the reasoning. Fixed price, measured in days. It is the cheapest way to find out what you can promise before you build on it.
Bespoke policy packs. Authored and calibrated against your traffic, not against a demo corpus. Including the contrastive testing that catches the brand-token trap and the confirm-band collapse before they reach production.
Bespoke detectors. If our reference detector is not good enough for your domain, we will train one on your data and wire it in. PRIOR is designed for this: an external classifier can drive enforcement directly, on your own score scale, with no round trip in the serving path.
Agent and integration work. Scoping, tool-call boundaries, layered posture, and the awkward parts of putting a self-hosted model into production. Advisory or hands-on.
We built the thing, so we know where it bends.
Steve has spent his career as a software consultant, and Eagle Logic builds PRIOR, a behavioral control plane that enforces policy inside open-weights models. The methodology on this page is not theory. It comes from calibrating these policies, measuring where they hold, and publishing the places they do not. Our own capabilities and limitations page documents the boundaries we could not make work.
You do not need to be a PRIOR customer for the assessment to be useful. What comes back is a map of what is enforceable in your domain, and that is true whatever you enforce it with.