What brokeWhy it brokeHow to fix itFixed Zero noise
“Their CEO joined our alerts channel and never left. So when I asked for a feature on Friday night, it was live Saturday morning.”
“We were getting forty alerts a day. Now we get three a week, and the ones we get are real. So Foam sits in our team channel instead.”
“Before our users even get to ping us, Foam has already debugged the error and pointed us to the cause in our team channel.”
“I used to lose a day or two triaging Sentry bugs. With Foam, I see them the moment they matter, already root-caused.”
The Problem
Your observability stack is stuck in 2018.
Coding agents now read your telemetry. The data layer they’re hitting was designed for humans staring at dashboards, not agents looking for signal.
Your traces are sampled, your alerts are noisy, and your services are partially instrumented. It was tolerable when humans were the only consumer. Now agents are hitting the same data, missing things humans would catch, and so you’re still up at 3am cleaning up after both of you.
You shouldn’t have to build to bruteforce your way out of it.
You wire up MCPs and build an agent to fix bugs. It works half the time. It feels promising. But it does not alert your team, so you add a monitoring agent on top to invoke it.
After only two weeks of building your agent, your token bill has blown past what you projected (have you checked?) and nobody on your team has time to build evals so your agent is sometimes right. And why should they? This is not your core product.
After all this work, you are still getting billed for glorified data storage incumbents call “observability.”
How It Works
We rebuilt the pipeline from ingestion to fix.
Full ingestion. No sampling. No indexing bill.
Our agents see all of the telemetry, not a 1% sample. Every span, log, and metric is ingested and processed so it is available for correlation and root causing. We made this economically feasible by building with principles oriented toward agents, not humans.
Group by cause, not call path.
We built new programmatic error clustering with AI in mind. Then, after Foam is done root causing, it continues to further cluster issues by root cause, not just signature. This means Foam can point out a single root cause for three services failing on one upstream.
Measured RCA performance, not log summarization.
Our agents correlate logs, traces, metrics, and code. Every change has to outperform the previous release against a benchmark of hundreds of real production cases before it ships. We use these benchmarks to drive improvements in both latency and accuracy.
Route to who is responsible, not who touched it last.
Foam triages after clustering, determining criticality and root cause while understanding your business logic and context. It maps recent changes, cross-service dependencies, and error ownership to attribute accurately to the right engineer, not whoever last touched the line in the stack trace.
Configuration that calibrates itself.
Foam has no manual configuration. It learns baselines from live traffic and alerts fire when behavior deviates from the baseline. After root cause, duplicates are suppressed and priority is re-scored.