u/Icy-Equipment-6213

Without execution control:

- timeout → retry

- retry → duplicate side effect

- duplicate side effect → real damage

Example:

agent charges a customer

API times out

agent retries

customer gets charged twice

AgentGate treats uncertain execution as:

unknown_effect

which forces reconciliation before retrying.

The key shift:

agents shouldn’t decide if execution succeeded.

the runtime should.

Built a small demo around this tonight.

https://preview.redd.it/hmqiu4puqiyg1.png?width=1920&format=png&auto=webp&s=338464845ae010d79b35ca74af6dd98021a0efc6

I've been building custom ai agents for fraud detection at my company, the most constant and frustrating problem was the agent worked properly with every workflow end to end successfully in local/demo but when we moved to prod the agent immediately failed after 1 week, and the reason was it hit flaky apis, and lost state, loosing context and hallucinating past state. It costed us a lot because the cascading error were crazy and the whole workflow broke due to it. I still remember it was disastrous. Curious you all are handling these issues?

I've been building custom ai agents for fraud detection at my company, the most constant and frustrating problem was the agent worked properly with every workflow end to end successfully in local/demo but when we moved to prod the agent immediately failed after 1 week, and the reason was it hit flaky apis, and lost state, loosing context and hallucinating past state. It costed us a lot because the cascading error were crazy and the whole workflow broke due to it. I still remember it was disastrous. Curious you all are handling these issues?

I've been building custom ai agents for fraud detection at my company, the most constant and frustrating problem was the agent worked properly with every workflow end to end successfully in local/demo but when we moved to prod the agent immediately failed after 1 week, and the reason was it hit flaky apis, and lost state, loosing context and hallucinating past state. It costed us a lot because the cascading error were crazy and the whole workflow broke due to it. I still remember it was disastrous. Curious you all are handling these issues?

I've been building custom ai agents for fraud detection at my company, the most constant and frustrating problem was the agent worked properly with every workflow end to end successfully in local/demo but when we moved to prod the agent immediately failed after 1 week, and the reason was it hit flaky apis, and lost state, loosing context and hallucinating past state. It costed us a lot because the cascading error were crazy and the whole workflow broke due to it. I still remember it was disastrous. Curious you all are handling these issues?

>

I’ve been deep in the trenches building out multi-step agentic workflows, and I’m hitting a consistent wall with what I can only describe as "stochastic decay."

The pattern is frustrating: Runs 1 through 3 execute flawlessly, but by the fourth iteration with the exact same input and code the agent spontaneously decides to skip a critical validation gate or misconfigures a tool call. It feels less like traditional software engineering and more like debugging a high-entropy system with unintended side effects. Even with robust logging and retries implemented, I’m often left staring at the traces without a clear "ground truth" on why the reasoning path diverged or what the deterministic expectation should have been at that specific node.

The real headache, however, is handling Human-in-the-Loop (HITL) approval flows. When I pause an action say, an agent deciding to email a customer about an overdue invoice and approve it three hours later, the state of the world has often shifted lol. If the customer paid in that interim, the approved action is now a liability. I’m currently stuck in a design loop between three suboptimal choices: executing the stale approval (risky), forcing a manual state re-check (extra latency), or re-running the entire reasoning chain (which risks further trajectory drift).

I’m curious how you are all handling :

1.Deterministic Control vs. LLM Retries: Are you moving toward strict state-machine constraints to keep the agent on the rails?

2.Approval + Resume Semantics: How are you handling temporal consistency when an agent "wakes up" after a long pause?

3.Production Guardrails: What are the most effective ways you've found to prevent agents from doing something objectively dumb in a live environment without killing their autonomy?

>

We’ve been talking to AI builders, and a recurring pain is the overhead of managing tool calls retries, approvals, schemas, security. What’s the biggest friction you run into when orchestrating AI agent tasks? drop your biggest hurdles in the comments.

I’m tired of “vibe-checking” my agents.

Been building some agent workflows and the worst part isn’t writing them, it’s reliability.
It works 3 times, then randomly:

1.hallucinates a tool call

2.skips a validation step

3.or just takes a completely different path

No code changes. Same input. Different behavior.

Tools like LangSmith or Sentry help debug after it breaks, but I still don’t have a good way to answer: Will this agent behave consistently before I ship it?

How are you guys actually validating agent reliability today?

1.just replaying runs?

2.writing custom tests?

3.or accepting the randomness?

I’m tired of “vibe-checking” my agents.

Been building some agent workflows and the worst part isn’t writing them, it’s reliability.
It works 3 times, then randomly:

1.hallucinates a tool call

2.skips a validation step

3.or just takes a completely different path

No code changes. Same input. Different behavior.

Tools like LangSmith or Sentry help debug after it breaks, but I still don’t have a good way to answer: Will this agent behave consistently before I ship it?

How are you guys actually validating agent reliability today?

1.just replaying runs?

2.writing custom tests?

3.or accepting the randomness?

I’m tired of 'vibe-checking' my agents.

I’ve been building a few complex agentic workflows lately, and the most frustrating part isn't the initial code, it's the non-deterministic drift. It works 3 times in a row, then on the 4th run, it hallucinations a tool call or skips a critical validation step for no reason.

Standard observability (LangSmith/Sentry) is great for seeing how it broke after the fact, but it doesn't help me verify reliability before I push to prod.

Curious if any of you faced this type of problem.