Operations

Agent observability vs application monitoring

Application monitoring tells you whether the service is healthy. Agent observability tells you whether the agent did the right thing — and lets you reconstruct how it decided. Teams running agents need both, and the gap between them is where agent incidents hide.

Dimension Agent observability Application monitoring
Core question Did the agent do the right thing? Is the service up and fast?
Unit of analysis Task: reasoning steps, tool calls, model calls Request: latency, errors, throughput
Failure it catches Confidently wrong actions with healthy metrics Outages, slowdowns, error spikes
Primary consumers Agent engineers, evaluators, auditors SRE and on-call engineers
Data sensitivity High — captures prompts, tool inputs/outputs Lower — mostly metadata and metrics
Typical tooling Tracing with agent span conventions, eval pipelines APM, metrics, log aggregation

The verdict

This is not a choice between two products but between two layers. Keep application monitoring for the infrastructure an agent runs on; add agent observability the moment an agent takes actions that matter, because that is the moment "the service is healthy" stops meaning "the agent is behaving." If budget forces sequencing, instrument the agent's tool calls first — that is where consequential actions happen and where post-incident questions land.

Frequently asked questions

Can I get agent observability from my existing APM tool?

Partially. APM tools ingest traces, and OpenTelemetry agent conventions are emerging, so step-level spans can land in existing backends. What APM tools lack is the agent-specific layer: evaluation hooks, prompt and tool-call capture with redaction, and cost-per-task views.

Is your organisation ready for AI agents?

Take the assessment →