Updated 2026-09-11
AI agent observability architecture
AI agent observability architecture ties plan, retrieve, tool, gate, and deliver spans to task outcomes. Infra metrics alone cannot explain why an agent failed.
Low latency does not prove an AI system is healthy.
Trace the AI path
Instrument each stage with correlation IDs that survive retries. Attach tool names, arguments classes, and approval decisions.
Taxonomy beats raw logs
Classify tool misuse, permission denials, timeouts, loops, and human overrides so operators know what to fix.
SLOs beyond latency
Define service levels on task success and containment events. A fast wrong answer is still a failure.
Join observability to control
When failure rates spike, feature flags and kill switches should be one action away from the same console.
Related Knackline capability
After the diagnosis, see how Knackline hardens this class of system in production.
Open observability capabilityRelated reports
Agent observability: traces that show what the agent did, not only latency
When something goes wrong, latency dashboards cannot tell you which tool call or plan step caused the damage.
AI Agent Security and Observability
Unreconstructable AI agent failures
SLO Design for AI Agent Workflows: Beyond Latency
Green p95 latency hides collapsing task success and rising human escalations
FAQ
- Can we reuse our APM stack?
- Yes as transport, but you still need AI-specific spans, taxonomies, and task outcomes. Generic HTTP traces are not enough.
- What should product and ops share?
- A shared failure vocabulary and dashboards keyed by workflow, not only by model endpoint.
- Where do evals fit?
- Offline evals gate changes. Online eval samples detect drift after deploy. Observability surfaces both to operators.