Modern engineering teams don’t have a data problem — they have a signal problem. Between logs, metrics, traces, and an ever-expanding mesh of microservices, most SRE and DevOps teams are drowning in telemetry while still getting paged at 3 a.m. for incidents nobody can explain in under an hour. That’s the gap AI observability closes. … Read more
Most teams treat observability as something you add after an agent is already in production — a dashboard bolted on once something breaks badly enough to get attention. That approach works for traditional software because failures are mostly binary: a service is up or down, a request succeeds or throws. It doesn’t work for AI … Read more
Enterprise AI agents don’t fail like traditional software fails. A microservice either crashes or it doesn’t — you get a stack trace, a 500 error, a clear signal. An AI agent can “succeed” from a systems perspective (200 response, no exception, reasonable latency) while quietly hallucinating a refund amount, leaking a customer’s PII into a … Read more