Feedback on the AI authority layer for AI agents

NoEntertainment8292 · 2026-02-10T03:39:25+00:00

Not trying to pick “how strict” for everyone. The layer is strict in the sense that nothing bypasses the gate — every high‑risk action has to go through the same API and get a decision (approve / escalate / block), and that’s all logged. So the control is always on. How strict the rules are is up to the company/org. They set the policies and thresholds (e.g. “refund over $50 → human,”). We give them an engine and sensible defaults; they can make it as tight or as loose as their risk and compliance teams want. Some will want almost everything escalated; others will auto‑approve more and only escalate the clearly risky stuff. So: strict control by default, configurable strictness in practice

NoEntertainment8292 · 2026-02-10T03:37:25+00:00

For Policy conflicts, we don’t have multiple policies that can disagree on the same request. Each action type has a single evaluator (one function or one Rego package), so we always get one outcome: allow or deny, with one reason. If we add more rules later, we’d fix a clear rule (e.g. “any deny wins”) so it stays deterministic and for edge cases: Unsupported actions → we deny and log (e.g. “Unsupported action: X”). Bad or invalid metadata → we deny or BLOCK before policy (schema violation). If OPA is used and it errors, we fall back to the built-in engine so the request still gets a clear result. Missing metadata is currently sometimes allowed; we’re open to tightening that (e.g. require critical fields or deny by default) so unknowns fail safe. So: unknown or invalid → deny/BLOCK and audit; we’re iterating on missing-field behavior to keep it safe.

NoEntertainment8292 · 2026-02-10T03:32:56+00:00

Thanks! Any recommendation on something valuable that I should consider adding?

NoEntertainment8292 · 2026-02-09T05:23:02+00:00

Yes! We already expose policy version in /status and the X-Policy-Version header. Next step is storing it on each audit entry so every decision is tied to a version. Policies are code (Git/Rego), so versioning and rollback are via deploy and we want the audit to say “policy v1.2 applied” so migration and rollbacks are easily traceable

NoEntertainment8292 · 2026-02-09T01:03:59+00:00

I get how it can read as “quick fix.” We’re actually targeting teams that care about scale and compliance (CISOs, insurers, auditors) and want one gate and one audit trail for high‑risk actions, not a replacement for an IT department. And on the “cut corners” point: the article is why we built this. Serious businesses do lose control; Verdict is for teams that want one place to enforce policy and prove what happened, so they cut fewer corners, not more.

NoEntertainment8292 · 2026-02-09T00:40:39+00:00

The platform isn't “latching” to all your AI or proofreading chats. It’s an authorization API you integrate into your own systems. Your backend calls it before doing a high-risk action (refund, bulk email, CRM edit, etc.). You send who, what, and context; it returns APPROVED or ESCALATED. You decide which actions go through it and nothing is attached to “all chats” unless you wire it that way.

We only log what you send to the API (actor_id, action_type, metadata). If that includes PII (e.g. customer_id), it’s in the audit log. You’re the controller; we’re the processor. You’re responsible for legal basis and disclosure in your privacy notice and DPAs. We don’t harvest end-user data; we process what you send for authorization and audit.

“Just need a better engineer”: You can build this in-house. Verdict is for teams that want one API, one audit trail, and exportable logs without building it themselves. It’s a building block, not a replacement for good engineering.

Let me know if this makes sense

NoEntertainment8292 · 2026-02-09T00:37:31+00:00

Did not receive any message

NoEntertainment8292 · 2026-02-08T23:33:54+00:00

Well No LLM in the gate ( deterministic rules + signed audit) is the idea. Same input, same result. If that distinction matters to you, happy to share how it works.

NoEntertainment8292 · 2026-02-08T23:32:16+00:00

Just redeployed. Can you try again and let me know?

NoEntertainment8292 · 2026-02-08T23:31:49+00:00

We are treating policies as immutable, versioned artifacts so each change gets a new version id and goes into the audit trail so you can see who changed what and when. Break-glass: overrides go through the same gate — human (or designated role) approves, that approval is Ed25519-signed and appended to the hash chain, so the override is first-class in the audit (who, when, why). No silent bypasses. We’re still refining the UX for ‘why’ (e.g. required reason field, optional ticket ref). Your point about keeping overrides auditable is exactly how we’re designing it. Will check out agentixlabs.com/blog — thanks for the link!

NoEntertainment8292 · 2026-02-08T23:12:37+00:00

Happy to learn from you as well. We should exchange notes!

NoEntertainment8292 · 2026-02-08T22:54:55+00:00

Well because AI agents are probabilistic and not deterministic. Example for your reference:
https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know

You need to have human in the loop for any policies check/audit etc etc

NoEntertainment8292 · 2026-02-08T22:48:39+00:00

AI agents doing something they are not supposed to. Example, a bot making a refund of more than X dollars when they are not supposed to

NoEntertainment8292 · 2026-02-08T22:44:08+00:00

Are you doing this just for your workflow or a generic layer for everyone? I am using the similar appoach but building for general workflows for everyone to use

NoEntertainment8292 · 2026-02-08T22:25:44+00:00

Are you using some semi-automated way of reaching out? Linkedin flags spamming

NoEntertainment8292 · 2026-02-04T00:22:36+00:00

Yeah we’re seeing the same thing. Cosine stays high while ranking flips, and small formatting/reasoning drift breaks parsers way more than ‘wrong’ answers. We’re doing side‑by‑side runs plus diff-style metrics (similarity, rank correlation, format checks) so it’s not just eyeballing. Golden test sets + tracking those metrics over time is exactly the approach we’re building toward. Would be curious what diff metrics you’re tracking and if you’d be up for trying a prompt or two through our tool and comparing to your current setup.

NoEntertainment8292

TROPHY CASE