u/hack_the_developer

Hey r/syrin_ai - I am the founder of Syrin AI. I will be using this subreddit to build in public, share what we are learning, and get direct feedback from developers.

Let me start with why I built this.

A developer I know shipped an AI agent to handle customer support for a SaaS product.

It went live on a Friday. It ran all weekend. It gave wrong answers for 60 hours.

He found out Monday from a furious client message.

When I asked if he had any monitoring, he said "I had logs. I just never thought to look."

This keeps happening.

We are shipping AI agents like they are static websites. Build them. Deploy them. Hope they work.

But agents are not static. They are decision-making systems that run in production, talk to your users, call your APIs, and make choices on your behalf. Every single minute they are live.

And most teams have zero visibility into any of it.

This is the problem Syrin solves.

Mission control for AI agents. Traces, Experimentation, Governance.

Agent Config is free forever. We have a paid pilot open until June 6.

I want to know, is this a problem you are facing? What does your current agent monitoring look like? Be honest. I am not here to pitch. I am here to learn.

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.

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.

The gap nobody's solved: observability tools log what happened. Eval frameworks test offline. Nothing lets you run controlled experiments on real production traffic - across prompts, models, temperature, agent topology - simultaneously.

That's Syrin. We're giving free access to 5 teams actively running agents in production/dev who will push it hard and tell us what's broken.

If you're burning hours on regressions you can't isolate - drop a comment or DM.