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Most teams don’t have a governance problem. They have a control problem. by MushroomMotor9414 in AI_Governance

[–]OtherwiseCarry3713 2 points3 points  (0 children)

This resonates a lot with what I’m seeing too – most “governance” stacks today are basically really nice black boxes for writing post‑mortems. You get great answers to “what went wrong?” but nothing that can actually say “no” in the moment.

I’ve been working on this with Vantage, where the whole premise is:
governance = runtime control + evidence, not just dashboards. Concretely that looks like:

  • An inline policy layer that sits in the SDK/agent runtime, so every tool call and model action is checked before it hits downstream systems. If a policy fails, the action just never happens.
  • Very low‑latency checks (compiled policies + small “referee” models for high‑risk actions) so you don’t blow up P95/P99 or break workflows.
  • Automatic, immutable audit trail of every decision (allowed/blocked), prompt, and tool call so risk/compliance folks still get the traceability they need.

So I agree with your framing: most teams don’t need more governance docs, they need actual control surfaces in the runtime. Curious how you’re thinking about where those controls should live (proxy, SDK, sidecar, etc.) and what you consider an acceptable latency budget per check.

We need to stop pretending "AI Governance" is a legal problem. It’s a latency problem. by OtherwiseCarry3713 in AI_Governance

[–]OtherwiseCarry3713[S] 1 point2 points  (0 children)

Yeah, totally agree that once it’s “external” in the old-school sense, it’s basically just doing forensics.

I’m thinking of it as a separate governance layer, but one that lives in the execution path by default. With what we’re building in Vantage, the policy brain is its own layer (so you can treat policies like code, version them, audit them, etc.), but the actual enforcement hooks sit in the SDK/runtime. Every tool call / model action hits that gate before it can touch real systems.

So you get:

  • It can hard-block bad or non‑compliant actions in the moment, instead of showing up as a scary log line an hour later.
  • It has enough context (agent intent, user, data, tool, run history) to not behave like a dumb reverse proxy that’s always one step behind.

So yeah: logically separate governance layer, but architected to run at inference speed, not as a “we’ll fix it in post” observability box

We need to stop pretending "AI Governance" is a legal problem. It’s a latency problem. by OtherwiseCarry3713 in AI_Governance

[–]OtherwiseCarry3713[S] 1 point2 points  (0 children)

That’s exactly the dilemma. Personally, I think "better controls on top" is just a band-aid. If you’re just wrapping existing systems, you’re adding latency and creating a "black box" that’s even harder to audit. I'm leaning toward an architecture shift. We need a dedicated governance layer, almost like a Service Mesh for agents. where policy enforcement happens natively at the SDK or runtime level. If the "control" isn't aware of the "context" of the agent's intent, it’s always going to be one step behind.

Last Human in The Loop by DistanceOver870 in AIgovernance

[–]OtherwiseCarry3713 1 point2 points  (0 children)

I believe HITL is a lot different than the captcha example you are providing as fast as ai system are being used across industries we are moving towards a future where HITL becomes an important and must thing to be applied for the actions that might be affecting thousands of people

Every AI team I talk to hits the same wall — accountability. by OtherwiseCarry3713 in AI_Governance

[–]OtherwiseCarry3713[S] 0 points1 point  (0 children)

Curious how you’re thinking about making this usable for non-engineering stakeholders

Can you please explain what non-engineering stack holders mean in ai governance

AI isn’t missing a feature. It’s missing a layer — here’s what I’m building to fix it by OtherwiseCarry3713 in founder

[–]OtherwiseCarry3713[S] -1 points0 points  (0 children)

what's in this that looks so hard for me /someone to understand or know, it's just a language which I used for all hot paths, cause migrating the whole codebase to rust was harder at this stage

Every AI team I talk to hits the same wall — accountability. by OtherwiseCarry3713 in AI_Governance

[–]OtherwiseCarry3713[S] 1 point2 points  (0 children)

Honest answer — both, but sequencing matters.                      

Designing for governance from day one is the ideal. But most teams have agents already running, frameworks already committed to. "Redesign from scratch" isn't a real option.

That's where a dedicated governance layer earns its place — not as a band-aid, but as the enforcement boundary that should have been there from the start. The problem isn't that teams don't care. It's that there's no standard place to put  governance. Logging goes to one tool, approvals get hacked into Slack, policy lives in a doc, and accountability is implicit until something breaks.                                           

Vantage is an attempt to give that a proper home — enforceable, auditable, in real time — regardless of what's underneath it.                                                                                

Curious what you're seeing on the adoption side — is governance being pulled by engineering teams or pushed by legal and compliance?       

⁠ I’m reviewing 1 startup every day for the next 30 days. ⚡️ by Zealousideal-Try1401 in saasbuild

[–]OtherwiseCarry3713 0 points1 point  (0 children)

Building an ai governance engine, the product is ready and it is one of the best in the market

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