An AI agent turns one user-visible task into a sequence of hidden operations. It may call a model, choose a tool, query data, retry, call another model and only then produce an answer. Observability has to follow the workflow, not only the final request.
The unit of analysis changes from request to run
For a simple LLM feature, one request often maps closely to one model call. For an agent, the useful unit is the run or trace.
A trace groups the steps that contributed to one outcome. Without that grouping, teams can see individual calls but cannot explain why the workflow took 30 seconds, used six model calls or failed after a tool invocation.
Capture model calls and tool calls in the same trace
Model latency is only part of agent latency. A database query, web request, retrieval step or internal service may dominate the total run.
OpenTelemetry's Generative AI telemetry work includes model operations and tool-call context, which reflects this shift toward multi-step systems. A trace should make it possible to identify which step consumed time, tokens or retries.
Track cost per outcome
Agent cost is often nonlinear. One difficult task may trigger additional planning or retries while another resolves immediately.
Measure calls per run, input and output tokens per run, tool calls per run and cost per successful outcome. These metrics show whether the workflow is becoming more expensive even when the number of user requests is stable.
Watch for loops and retry amplification
Agents can enter patterns where a tool fails, the model replans, the same tool runs again and the workflow consumes more time and tokens.
Guardrails such as maximum steps, retry limits and time budgets help, but the observability layer should make the loop obvious. A trace that visually repeats the same operation is easier to investigate than a flat list of logs.
Quality and reliability need separate signals
A technically successful agent run can still produce a poor outcome. Operational telemetry answers whether the system completed, how long it took and what it cost. Quality evaluation answers whether the result was useful.
Teams need both, but they should not be collapsed into one metric. Reliability problems and quality problems often require different fixes.
The executive question becomes accountability
As agents take actions, stakeholders ask new questions: What did the agent do? Which tools did it access? How much did the run cost? Which policy applied? Who owns the workflow?
Agent observability is therefore not only debugging. It becomes part of governance and operational accountability.
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