My warm-up blog for the Agents of SigNoz hackathon by WeMakeDevs x SigNoz.
Here's a claim I'll defend for the rest of this post: the moment you wrapped an LLM in a loop and gave it tools, you stopped building an application and started operating a distributed system — one that is non-deterministic, fans out across network boundaries you don't control, and bills you per token for the privilege. Most teams are running these systems in production with less instrumentation than they'd put on a toy CRUD app. That gap is where the 2 a.m. incidents, the mystery invoices, and the silent quality regressions live.
I want to make the case, concretely, for why AI agents demand observability, what the standards-based way to do it looks like in 2026, and why an OpenTelemetry-native backend like SigNoz is the right place to send the data.
The failure modes traditional monitoring cannot see
A conventional service has a bounded, mostly-deterministic execution path. Your existing APM catches the things that go wrong there: a 500, a slow query, a memory leak. Now look at what an agent actually does on a single user request:
