Mayank Bansal

Cool. We're working on a similar space with Failproof AI, but from the runtime reliability side. It's open source, and we've found that observability and runtime guardrails complement each other really well in production. Please checkout our open source repo of Failproof AI and make the star if you like it :)

Really liked the distinction between "is the system up?" and "is the system right?" That's the gap we've been seeing too. One thing I'd add is that observability tells you what happened, but production systems also need a runtime layer that can stop obviously bad actions before they execute. Feels like both are becoming essential as agents move beyond demos

Ahmet Özel I like the phrase "the contract is what proves it is useful." That's exactly the mindset shift that led us to open source Failproof AI. AI can generate implementations incredibly fast, but the confidence comes from independently verifying the behavior instead of trusting the generation. Please check out our repo of Failproof AI, u will surely found it helpful

I think we're starting to see two separate infrastructure layers emerge. One is observability capturing traces, prompts, retrieval context, and behaviour after the fact. The other is runtime enforcement policies, completion validation, and execution checks before an action happens. Neither replaces the other, but together they make production agents much easier to trust.

I had the same realization recently. The moment an agent gets access to real infrastructure, it stops being a prompting problem and starts being an infrastructure problem. We've been moving more and more of those decisions into runtime policies in Failproof AI instead of hoping the model remembers the rules.

This is exactly the kind of thing runtime guardrails are for. I'm making Failproof AI, use policies to block access to sensitive files like .env before the agent ever gets to execute the read. It's completely open source just check out the repo of Failproof AI. Hit the star also if helpful :)

That's been one of the biggest design goals for us. We treat false positives as bugs in the rule, not something the user should just live with. The rules are deterministic, so if a check fires incorrectly, it'll do it consistently, which makes it much easier to reproduce, debug, and either improve the rule or add a narrowly scoped exception. I'd much rather have a predictable false positive than an LLM making a different judgment every run.

Lmao, I know the feeling. We've gradually moved away from "LLM checks another LLM" and toward deterministic runtime checks for anything that matters. Completion validation, execution policies, and progress checks have been much more reliable in our experience. Checkout our Failproof AI repo, love to hear feedback from you. Mark star also if it's helpful to you ;)

Great write-up. I completely agree that deterministic validation belongs outside the model. We've been exploring something similar with Failproof AI, but focused on runtime behavior after the code leaves the editor—things like false completion, low-progress loops, and execution policy checks. Feels like deterministic code review and runtime validation solve complementary parts of the same reliability problemv