u/haletronic

I’m looking for a collaborator / co-founder to help pressure-test and position a system I’ve been building for AI agent execution control and governance.

Core problem
AI systems suggest, decide, and execute with the same authority. In practice, that means an agent can be over-permissioned and capable of taking real actions against production systems, data, APIs, filesystems, etc.

I built a runtime boundary that sits between AI and the actions/capabilities it wants to invoke. Every action request is evaluated before execution and either proceeds or fails closed.

The model decides intent.
The runtime decides what is allowed.

Current proofs and demos show:

• deterministic allow/deny enforcement
• bounded authority
• workload neutrality (same enforcement for assistants, scripts, agents, etc.)
• execution-time policy evaluation
• replayable decision paths and traceability foundations

What I need help with now:

• how to position this
• where it fits
• who actually feels this problem today
• how to communicate it clearly to the right audience

I’m especially interested in talking with people who think deeply about:

• AI systems
• platform engineering
• infrastructure and security boundaries
• governance and authorization
• enterprise risk
• operational trust in automation

Not looking for hype or “AI wrappers.”

More interested in talking with people who immediately understand why execution authority is becoming a real problem.

Recently a coding agent used by PocketOS deleted their production database—and its backups—in about nine seconds. This wasn’t an AI failure. It was a system design failure. The agent didn’t “go rogue”—it did exactly what it was allowed to do. That’s the uncomfortable part.

Most agent setups still look something like this: an LLM generates intent, that gets passed to a tool or script, and that tool has direct access to real systems—databases, filesystems, APIs. There are controls, but they sit around the edges. Prompts tell the agent what not to do. Some validation tries to catch obvious mistakes. Sometimes there’s a confirmation step. Logs tell you what happened after the fact. None of that actually decides whether something is allowed to run.

So when something goes wrong, it doesn’t slow down or fail safely—it just runs. And if the same path can modify production data or delete backups, the system was already in a bad state before the agent even made a decision.

If you’ve worked with these models, it’s easy to default to prompt fixes. Something breaks, so you tighten instructions and add guardrails. But most “guardrails” are just suggestions. If the agent can ignore them and still execute, they weren’t guardrails—they were advice.

What’s missing is an execution boundary. Somewhere every action gets checked before it runs, and the system—not the agent—decides yes or no. Without that, you’re handing real authority to something that’s inherently probabilistic.

That’s the shift I think we need. Not better prompts. Not more logs. Systems where certain actions simply can’t run without explicit authorization.

Because once execution starts, it’s already too late.

The problem isn’t model behavior—it’s the absence of enforced execution boundaries. That’s what I’ve been spending time on: making that decision point explicit—and actually enforceable.

Recently a coding agent used by PocketOS deleted their production database—and its backups—in about nine seconds. This wasn’t an AI failure. It was a system design failure. The agent didn’t “go rogue”—it did exactly what it was allowed to do. That’s the uncomfortable part.

Most agent setups still look something like this: an LLM generates intent, that gets passed to a tool or script, and that tool has direct access to real systems—databases, filesystems, APIs. There are controls, but they sit around the edges. Prompts tell the agent what not to do. Validations try to catch obvious mistakes. Sometimes there’s a confirmation step. Logs tell you what happened after the fact. None of that actually decides whether something is allowed to run.

So when something does go wrong, it doesn’t slow down or fail safely—it just runs. And if the same path can modify production data or delete backups, the system was already in a bad state before the agent even made a decision.

If you’ve worked with these models, it’s easy to default to prompt fixes. Something breaks, so you tighten instructions and add guardrails. But most “guardrails” are just suggestions. If the agent can ignore them and still execute, they weren’t guardrails—they were advice.

What’s missing is an execution boundary. Somewhere that every action gets checked before it runs, and the system—not the agent—decides yes or no. Without that, you’re handing real authority to something that’s inherently probabilistic.

That’s the shift I think we need. Not better prompts. Not more logs. Systems where certain actions simply can’t run without explicit authorization.

Because once execution starts, it’s already too late.

The problem isn’t model behavior—it’s the absence of enforced execution boundaries. That’s what I’ve been spending time on: making that decision point explicit—and actually enforceable.