We watched our own agents fail.
Synaptico started the way most useful tools do — a small team got burned, and built the thing they wished existed.
We were running a research agent over a few hundred documents a day. It worked in every demo. In production, it silently returned half-answers — a tool would time out, the model would quietly work around it, and nothing in our logs ever said why. We spent two weeks reading raw API responses before we found the pattern.
That was the problem: agents are non-deterministic, multi-step, and opaque. Your existing observability stack sees one HTTP request to a model provider. It does not see the eleven-tool dance that produced a wrong answer. Synaptico is the layer that does.
What we believe
- Agents fail quietly by default. Every production agent needs a system that notices when quality drops — not just when the process crashes.
- A failure you can't replay is a failure you can't fix. Reproduction has to take one command, not one afternoon.
- Your traces are yours. We never train on customer data, and we architect for self-hosting from day one.
- The model is the easy part. The hard part is everything around it — state, tools, retries, and knowing when to stop.
The team
Previously built internal agent platforms for teams shipping LLM products in production. Obsessed with making non-deterministic systems observable.
Spent a decade on distributed systems and tracing at scale. Believes every agent run deserves the same fidelity as a distributed trace.
Where we are
We're a small team, revenue-earning, and in private beta with a handful of teams shipping agents to real users. We're building on Claude because it's the model our customers build on first — and because agent reliability is a problem all of us share.
If you're shipping agents and you've ever said "it worked yesterday," we'd like to talk. Write to [email protected].