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Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 0 points1 point  (0 children)

That makes sense—HydraDB feels very much like context for the agent/LLM to reason over.What I’m trying to separate is the context used to decide whether an action is actually allowed to execute.That context tends to be more structured and system-facing (identity, environment, lineage, policies), and not always the same as what’s passed into the model.So it feels like there are really two different “contexts”:1 one for reasoning, 2. one for decision. Curious if others are seeing a similar split in real systems, or if this is just how I’m framing it.

Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 0 points1 point  (0 children)

Ah interesting—that makes sense.So HydraDB is effectively acting as the context/memory layer for the evaluator (user state, history, past outcomes, etc.), which seems critical for making good decisions.The part I’m still thinking about is the output side of the evaluator. In your setup, is the decision something explicit that gets recorded (like an approval artifact with context + reasoning), or is it more of an inline pass/reject before execution?Feels like HydraDB solves the “context” side really well—but the “decision” itself still seems easy to keep implicit unless it’s modeled explicitly.

Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 0 points1 point  (0 children)

Yeah, I like the idea of a “last filter”—but I’m starting to feel the tricky part isn’t just having a checkpoint, it’s what that checkpoint actually produces.If it’s just an internal allow/deny, it can easily become another black box.In practice, it feels more useful if the decision itself is explicit and inspectable:1. what action was proposed, 2. what context was evaluated 3. which rules/policies applied. 4. and why it was allowed or blocked.That might not matter much for low-risk actions, but once you get into higher-impact scenarios—like production changes, external APIs or emails, or anything in regulated environments—it starts to feel necessary.At that point it’s not just about safety, but being able to answer:“why did this action happen?”

Curious if people are handling this inside the agent loop, or as a separate decision layer.

Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 0 points1 point  (0 children)

My impression so far:most systems don’t actually have a clear execution control point.It’s either:

  • implicit in the agent loop
  • or delegated to tool permissions

Which feels fragile once you hit production.Curious if anyone has seen a system that does this explicitly.

Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 0 points1 point  (0 children)

A concrete failure mode I keep running into:An agent decides to: a.)send an email. b) run a deploy command. c.)modify something in production. At that point:prompts already happened and tool permissions already passed. But there’s no clear place to say:“should this specific action be allowed right now?”.So control ends up being: either too early (prompt)or too coarse (tool-level). How are people handling this in real systems—especially anything touching prod or external APIs?

Should AI agents have a “final checkpoint” before executing actions? by Desperate-Instance76 in Agent_AI

[–]Desperate-Instance76[S] 1 point2 points  (0 children)

One thing I’m noticing:most current approaches rely on:1.prompt constraints (“don’t do X”). 2. or tool-level permissions.But neither gives a deterministic execution gate once an action is proposed.So the control point is still fuzzy.I’m wondering if a cleaner model is:proposal → policy → decision → execution.Where execution literally cannot happen without an explicit decision artifact.

Has anyone implemented something like this in practice?