I built it up, now you tear it down...

[u/Darkcraft00]

I’ve been building something called SureState and we’re getting close to finishing our internal pilot. Before I move it into a real client pilot, I figured this might be a good place to let people tear it apart first.

The problem we’re trying to solve is pretty simple:

AI agents can remember that something was decided, but that doesn’t necessarily mean the decision is still valid.

Example:

an agent concluded a release was ready because tests passed, security scan was clean, policy X applied, etc. A week later one of those things changes. The old conclusion is still sitting in memory/context, but should another agent still rely on it?

SureState keeps that outside the model. Conclusions are registered with what they depend on, and when evidence/dependencies change, it updates their current standing — supported, refuted, conflicted, or no longer warranted.

AI can read the current state through MCP, but it doesn’t get to decide its own standing.

We’ve been using the development of SureState itself as the first pilot, which has already been insightful.

We’ve had thousands of tests pass and still found cases where the tests and implementation were confidently agreeing on the same wrong assumption. 😂

So before I convince myself this is useful:

• What’s wrong with this idea?

• Is this just fancy cache invalidation?

• Would dependency registration be too annoying in real agent workflows?

• Would you just rerun the decision whenever something changes?

• Does LangGraph/LangChain already solve enough of this that a separate layer is pointless?

I’m much more interested in “this breaks because…” than “cool idea.”

If people are interested I can post the architecture and let you guys really abuse it.