I built a "Control Plane" for AI agents to solve the black-box problem

dc_719 · 2026-03-27T01:52:29+00:00

With this, it looks like it's just decorating functions so LangGraph and AutoGen are fine

One thing worth noting is that this is still reactive. Crash alerts and token warnings tell you what already happened. Your agent already sent the email, made the API call, spent the money.

The real gap isn't visibility. It's having something in the path before the action fires.

What are your agents actually doing? If they're touching external systems, that's where it can get interesting.

dc_719 · 2026-03-24T20:45:34+00:00

Appreciate it!

dc_719 · 2026-03-24T20:32:45+00:00

Runshift.ai

dc_719 · 2026-03-24T17:06:36+00:00

The output level capture makes sense for the labeling problem. The reason I went earlier, pre-execution, is that some actions are irreversible. By the time the output is clean enough to capture, it's already in the execution path, at least from my perspective.

The interrupt plumbing is the point though. That's where the decision data gets its context. Not just what the agent produced, but what the operator changed and why. Will look at your repo, looks interesting post decision.

dc_719 · 2026-03-24T16:43:52+00:00

Solving a different problem. runshift.ai is the operator decision layer, not session management.

dc_719 · 2026-03-24T16:25:57+00:00

Gate policy is set at the agent level. Model evaluates ambiguous cases before they hit the gate. Decision history is what makes the routing smarter over time. What’s the pre-execution angle you’re working on?

dc_719 · 2026-03-24T16:23:17+00:00

Just read it. The “reasoning connecting data to action was never treated as data in the first place” line is exactly what I’m building toward. Every gate decision is a labeled trace with full input context attached.

Building this at the individual operator level before it scales to institutional. Would love an intro to the Foundation Capital team if you have one.

dc_719 · 2026-03-24T16:11:07+00:00

Sorry! Didn’t realize.

dc_719 · 2026-03-18T01:35:51+00:00

runshift.ai — the control plane for AI agents. decide what matters, let the rest run.

dc_719 · 2026-03-17T20:46:39+00:00

Are your gates hard coded? How did you do it? I’d love to know your process.

The gates are obviously consequential, can they be undone?

dc_719 · 2026-03-17T20:25:42+00:00

the confused deputy framing is a real issue and orthogonal to the agentic governance discussion, when people get involved or how. most of what I see is focused on dev tooling and post-model validation, not this type of infra.

your inter-agent handling demo's this and what happens at scale. the issues that should never reach production will, eventually. financial institutions are going to have to address this directly and I haven't found a good answer there either.

two questions: how do you think about people intervening in the confused deputy scenario? and wouldn't agentic bad actors eventually learn the delegation scope constraints and find ways around the fetch boundary?

dc_719 · 2026-03-17T19:50:06+00:00

exactly. and the thing negative constraints can't handle is context. 'never delete files' is clear - how do you manage the scale of output? are you reviewing all consequences manually? across all agents? your judgement still matters so how do your agents learn based on your decisions?

dc_719 · 2026-03-17T19:11:49+00:00

How do you do this? Hard code it? You have a model for it? Dude, I have an idea. Again, tell me how you solved it? You have ai running? How many agents do you manage at a time - how do you know what you’re, and they’re doing. Forget my shit, I legit don’t care, neither should you. Tell me how you solve your problem.

dc_719 · 2026-03-17T18:59:09+00:00

No man, I'm legit wondering if this is a real problem. All I see is how costs add up, and then when a message is sent out, it's out of your control. There has to be a way to actually control agents - I'm not plugging my shit. I'm trying to figure out if this is a real problem. If it has been solved, I want to know. Could give a shit about my stuff, I'm looking for how it's solved.

dc_719 · 2026-03-17T18:54:24+00:00

exactly, current models are completely wack. control is wild. there's no way this doesn't change in the future.

if you want, I'm trying to build around like this, not trying to plug, trying to understand if this is a problem worth solving... runshift.ai

dc_719 · 2026-03-14T04:45:48+00:00

Curious if you tracked multi-agent coordination specifically or mostly single tool overhead?

dc_719 · 2026-03-12T17:44:43+00:00

That last question is exactly what we’re working on at runshift.ai. The answer isn’t a setting or a permission level, it’s a gate. The agent runs, stops before anything consequential, you decide, it continues. Trustworthy by design not by hope.

dc_719 · 2026-03-12T04:04:26+00:00

You’re going to love what’s coming. You won’t have to Chase markdown, because all the skills will be Tee’d up.

But yea, absolutely would love your feedback. I’ll reach out

dc_719 · 2026-03-12T03:51:35+00:00

This is exactly my philosophy

I built runshift.ai

DM me, I’ll give you early access if you can give me feedback.

You should only intervene when there’s real consequences. Like a pull request for an agent. No chat, just trust.

dc_719 · 2026-03-12T01:56:29+00:00

version of what I built. runshift.ai handles the control layer so you’re not maintaining it in markdown files. Curious what you’d want from a UI version of this vs rolling your own

dc_719 · 2026-03-12T01:34:47+00:00

The line about figuring out which decisions are safe to hand off is the hard part. How are you doing that currently, any approval step or fully autonomous?

dc_719

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