r/syrin_ai

I was building infrastructure for AI agent experimentation recently and ended up doing 50+ deep conversations with engineering teams across startups and Series B companies about what actually breaks in production and why. A few things that surprised me:

• most agent failures are not model failures
• prompt changes are often tested way more casually than normal code changes
• almost nobody fully agrees on who owns agent reliability
• teams underestimate the operational cost of flaky agents until customers feel it

Happy to talk about how teams run controlled experiments on prompts/configs, common production failure patterns, evals, reliability ownership, rollout strategies, and the economics behind all this.

Ask me anything.

Frameworks like LangGraph, CrewAI, and AutoGen have gotten pretty good at orchestration and execution. But almost none of them really help me figure out how to safely test prompt changes in production.

The default workflow for most teams (including mine) still seems to be like this - tweak a prompt, deploy it, watch metrics and hope nothing weird happens. But the problem is that when behavior changes, it’s hard to isolate why. If it was a prompt update or a model/provider change, or multiple parameters changing at once.

My team seems to treat agent config changes more like code deploys with versioned configs, baseline evals, gradual rollouts, traffic splitting, rollback support, etc. But honestly, most people I talk to are still doing this with logs.

Curious what others are doing here. Are you adapting any feature flag tools for agents? Building internal tooling? Or running eval pipelines? Feels like this layer of the AI stack is still pretty immature.

Been talking to 50+ engineering teams about production AI agent failures over the last few months. The pattern that keeps showing up: teams modify prompts and swap models regularly, but almost none run those changes as controlled experiments. When something breaks, there's no diff — just a production failure and a list of suspects.

The tooling gap is specific: observability tools log what happened. Eval frameworks test offline. Neither lets you run Variant A vs. Variant B on real production traffic, with actual variable isolation, before the change goes to 100% of users.

That's what we built. Syrin runs simultaneous experiments across system prompts, models, temperature, and agent topology on live traffic — with rollback triggers built in.

We're looking for 5 teams actively running multi-agent systems in production to use it for free and tell us what's broken. No SLA, no hand-holding — we want people who will push it hard and give honest feedback.

If you're spending time debugging regressions you can't isolate, drop a comment or DM me. Happy to get on a 30-minute call to see if there's a fit.