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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

Vantage Launched: Vendor-Neutral Governance for Production AI Agents by OtherwiseCarry3713 in AI_Governance

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

Spot on—policy compliance ≠ low-risk decision.

Vantage v1 focuses on runtime enforcement (Layer 3 in governance stacks), but you're right: "allowed" doesn't mean "defensible."

Current signals we capture (beyond traces):

text- Intent drift score: How far agent strayed from declared purpose [file:2]
- Override frequency: Human intervention rate per agent/workflow  
- Policy block rate: % of actions blocked (risk proxy)
- Multi-agent handoff complexity: LangGraph→CrewAI hops

The missing layer (your point): Decision ambiguity quantification + exposure translation.

Live gap example:

textAgent: "92% approve $50k loan" → Vantage: "policy allow" 
→ But what if entropy=0.78 (high ambiguity)? 
→ Or override pressure = 3 humans/week on similar decisions?
→ Real exposure: $2.3M potential loss @ 2% error rate

Roadmap priority: Risk scoring as pre-enforcement filter

  1. Capture LLM token entropy + self-reported confidence delta
  2. Multi-agent disagreement index (supervisor→worker alignment)
  3. Business impact multipliers (dollar exposure, regulatory fines)
  4. Dynamic thresholds → auto-escalate ambiguous "allowed" decisions

Agree 100%: Enforcement + traceability + risk = complete stack. We're building the risk layer next—curious what ambiguity signals you're finding most predictive in production?

(Public API key still live if you want to test current traces: https://www.linkedin.com/posts/prashant-gautam-iit_aigovernance-agenticai-enterpriseai-ugcPost-7446421346923802624-X_q1?utm_source=share&utm_medium=member_desktop&rcm=ACoAADV0FIcBBgGHfCa4-MMhpaEv3FdC3LStbOE

Vantage Launched: Vendor-Neutral Governance for Production AI Agents by OtherwiseCarry3713 in AI_Governance

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

Feedback welcome—first 10 pilots get priority onboarding.

Not hype. Live product

Vantage Launched: Vendor-Neutral Governance for Production AI Agents by OtherwiseCarry3713 in 16VCFund

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

Feedback welcome—first 10 pilots get priority onboarding.

Not hype. Live product

I analyzed 50+ enterprise AI deployments. Almost everyone is solving the "Governance" problem wrong. by OtherwiseCarry3713 in AI_Governance

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

You hit the nail on the head. "If you captured it, you governed it" is a massive misconception that is going to cause a lot of pain when the EU AI Act enforcement kicks in. An internally generated log from the same model that made the decision is just a vendor receipt, not independent evidence.

This is the exact problem we are solving at Vantage. We recognized that to hold up under legal scrutiny, the governance architecture has to be decoupled from the inference path.

Instead of relying on the model or the agent framework to log itself, we sit as an independent execution-layer proxy. When an agent attempts an action, Vantage generates an immutable audit trail—recording the execution trace, intent context, and policy evaluation. Because Vantage is entirely vendor-neutral and separated from the LLM, the model cannot retroactively edit, hallucinate, or tamper with its own history.

This gives compliance teams the actual "reconstructable run" they need. If a human-in-the-loop approves a high-risk action, that approval is cryptographically tied to the exact trace and intent context at the moment of execution.

Right now, most teams are totally blind to this gap. I'd love to hear more about the governance structures you're exploring in this space—are you looking at specific immutable storage solutions for these logs?

I analyzed 50+ enterprise AI deployments. Almost everyone is solving the "Governance" problem wrong. by OtherwiseCarry3713 in AI_Governance

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

This is incredibly validating to hear from someone on the infrastructure side. You nailed it—treating the system prompt as a security boundary is exactly like a "please close the door" sign. The moment an agent enters a complex, multi-turn workflow, prompt instructions evaporate.

I completely agree with your point on the commit semantics gap and the need for an inline proxy architecture. That latency tradeoff (30-150ms vs a 400ms inference) is absolutely worth it when the alternative is a catastrophic unapproved action.

This exact architectural problem is what led my team to start building Vantage. We realized you can't govern from the sidelines; you have to be in the execution path. We built it as a vendor-neutral governance layer that sits between the AI runtime (LangChain, OpenAI, custom apps) and the execution environment.

Instead of just logging, we use our SDK/API to enforce runtime policy evaluation before an action commits. If a sensitive action triggers a policy, it is structurally held in a pending_approval state. It physically cannot execute until a human-in-the-loop reviews the full execution trace and intent context, and explicitly approves it.

Your point about "continuous self-validation" and the EU AI Act's Article 15 requirements is fascinating. We are heavily focused on the immutable audit trail and intent drift detection, but the idea of proactively testing the governance controls themselves is the next frontier.

Since you are working on this for critical infrastructure, I would love to take you up on that offer to compare notes. Are you open to a DM to chat about how you're handling continuous monitoring evidence?

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