What is Agentic AI Observability?
ObservabilityInstrumentation that captures the full trajectory of an agent run: every model call, tool invocation, retrieval, guardrail check, and handoff, recorded in order with inputs, outputs, latency, and cost per step. Built for systems whose failures happen between API calls.
Why It Matters
API monitoring tells you the request succeeded. Agentic failure lives underneath that, in the steps the agent chose to take. A run that returns 200 OK in four seconds can hide twelve model calls, four tool invocations, and two handoffs, any of which may have gone wrong while the aggregate looked healthy.
The expensive failures are the quiet ones. One multi-agent task ran for eleven days and spent $47K while every trace stayed green, because nothing was watching what the runs contained. Observability for agents exists so that “the agent got stuck” is a question with an answer in the telemetry, not a week of archaeology.
What the Instrumentation Feeds
The artifact underneath is the trajectory: a span tree per run, recorded with OpenTelemetry’s GenAI conventions so traces land in whatever observability stack the team already runs. The practice is what gets attached to those traces. Eval scores ride on each run, so a behavioral drop is investigable instead of anecdotal. Cost is attributed per call and per tenant. Flagged actions carry decision records, the audit artifact that survives the run.
The vocabulary matters less than the destination. Traces that land in the team’s existing stack, with evals and cost attached, become operational data. Landed in a separate dashboard, they rot into screenshots people pull up during incident reviews.
Where It Breaks
The first break is ownership: telemetry without a weekly review cadence is a dashboard nobody opens, which is why observability and agentic drift are the same problem seen from two sides. The second break is the stopping point. Seeing what happened is not enough; a trace that cannot hand a flagged action to a policy decision is evidence without enforcement. That handoff to governance is where the practice ends or becomes theater.
How Flytebit Handles It
Full-trajectory tracing is the first thing we instrument in an observability engagement: every step captured as a readable run, with eval scores and per-call cost attached. The reasoning behind trajectory-first observability, and where it hands off to governance, is in Observability in Agentic AI.