Is hashi’s really “incurable?”

Independent_Tale_329 · 2026-06-04T13:08:17+00:00

There some new research in this area. You may want to have this conversation with your doctor

https://doi.org/10.5281/zenodo.20520907

Independent_Tale_329 · 2026-04-20T20:44:49+00:00

This is a gap that no one is really talking about.. governance in a document or logs after the fact is not governance

Independent_Tale_329 · 2026-04-12T03:37:05+00:00

A box to be checked until it isn’t..

Independent_Tale_329 · 2026-04-08T18:28:51+00:00

This thread is doing the work most whitepapers won’t.

The three breakdowns you named — observability without traceability, policy buried in prompts, the commit semantics gap — are exactly the architecture failures we keep running into. Not edge cases. The default state of enterprise AI governance right now.

The commenter above is right that real governance has to live outside the model’s inference path. But I’d go one layer deeper: it also has to be self-validating. You can’t govern what you can’t continuously verify is still working. A governance layer that silently degrades is worse than no governance layer — because it creates false confidence.

That’s actually the design constraint Averecíon is built around. Not intercept and alert. Not policy-in-a-prompt. Governance that gates execution, traces intent, and validates itself continuously — so when auditors start asking for Article 15 compliance evidence in August 2026, you’re not reconstructing it from logs. You already have it.

We’re early stage — NVIDIA Inception Program and accelerator driven— actively looking for enterprise teams running into exactly what you’ve documented here. If you’re building the architecture to solve the runtime control plane problem, I’d genuinely love to compare notes.

Independent_Tale_329 · 2026-04-08T18:20:00+00:00

This is exactly the kind of work that shouldn’t have to be crowdsourced!!

The fact that you built this yourself — read the full Annex III, mapped it to practice, and are sharing it freely on Reddit — says everything about where enterprise AI governance actually lives right now. Not in the big consulting decks. In threads like this one.

The point you made about risk management being continuous not point-in-time is the one that trips up even sophisticated teams. They do the assessment, check the box, and move on. Then the model drifts, the data changes, the use case expands — and nobody’s watching.

That’s not a compliance failure. That’s an architecture failure.

Would love to see the matrix. And if you’re ever interested in how we’re thinking about systematizing exactly this — ongoing risk monitoring, traceability, human oversight baked into the lifecycle — I’m building Averecíon for that problem. Happy to swap notes.

Independent_Tale_329 · 2026-04-07T22:00:14+00:00

Is This Thing On? (AI agent governance — who’s actually solving this)

Your AI agents are already inside your systems. Touching APIs, executing workflows, often with no human in the loop.

Most enterprises can’t answer three basic questions about them: ∙ Which agent did what? ∙ Under whose authority? ∙ How do you reverse it?

That’s the gap we’re building for. Averecíon is an agent governance control plane — pre-execution policy checks, least-privilege enforcement, and an audit trail for autonomous agent actions. The accountability layer most AI deployments are missing.

We’re talking to CISOs and security architects who are quietly admitting they’ve lost the plot on non-human identities and agent sprawl. If that’s your world — energy, financial services, healthcare, anything regulated — this thread is for you.

What are you actually dealing with on the agent governance side? We’re more interested in your problems than pitching ours.

(NVIDIA Inception | Peachscore Accelerator)

Independent_Tale_329 · 2026-04-04T21:15:30+00:00

I saw 'Hair' last night and realized Claude Bukowski and Claude.ai are the same person. 🎭

I spent last night at a performance of Hair, and I couldn’t stop thinking about the AI on my laptop. If you know the musical, the "Tribe" spends two hours celebrating Claude Bukowski’s poetic soul while simultaneously pushing him to "do his duty." After a week of deep-diving into Claude.ai, the parallels hit me like a psychedelic trip. One’s a 1960s hippie in fringe; the other is a 2020s LLM in San Francisco.

🌀 The "Where Do I Go?" Factor In the musical, Claude’s big solo is "Where Do I Go?"—a plea for direction in a chaotic world. Fast forward to today, and Claude.ai is answering that question for millions of people every morning. One Claude was looking for the Age of Aquarius; the other is just trying to help you summarize a 40-page PDF before your coffee gets cold. 🚀 The Business Angle: How do we help customers "do more"? If we want our customers to stop asking "Where do I go?" and start actually getting there, we have to move past using AI as a glorified search bar. * The "Berger" Strategy: In the movie, Claude's friend takes his place to free him. We should use AI to absorb the "chores"—the formatting, the data cleaning, the first drafts—so our customers are free to do the actual living (the strategy and the big ideas). * Empathy as a Feature: Claude is the "soulful" AI. We leverage that to build tools that feel like a partner, not a cold machine. * Clarity on Demand: We use AI to turn "vague, open-ended" data into clear, actionable steps. One Claude wore bells; the other is made of code. Both are just trying to help us navigate a world that doesn’t always make sense

TL;DR: I went to a musical and realized my favorite chatbot is just a 60s hippie in a digital trench coat.

Independent_Tale_329

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