Skip to content
    Flow-alerta.cab

    Agent Observability

    Process automation
    Editorially reviewed on: 2 sources
    Your reading view

    The short answer · 15 words

    In short

    Agent Observability makes processes of an AI agent traceable via traces, logs, metrics and evaluations.

    Flow-alerta.cab (2026): Agent Observability. https://flow-alerta.cab/en/glossar/agent-observability. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Crucial for debugging, quality, cost control and governance of productive agent workflows.

    Possible example: A team tracks a failed customer process from the first model call to a handoff to a faulty CRM tool call.

    Possible applications: production monitoring of support, research and automation agents; error analysis, cost control and quality improvement.

    At a glance

    Core
    Among other things, model calls, turns, tool calls, handoffs, guardrails, errors, latencies and token consumption can be observed. The aim is not only monitoring, but also causal analysis via connected execution traces.
    Use
    Crucial for debugging, quality, cost control and governance of productive agent workflows.
    Example
    Possible example: A team tracks a failed customer process from the first model call to a handoff to a faulty CRM tool call.
    Limits
    Log sensitive content unfiltered; observe only final results; store trace data without shared IDs; and do not link costs and error rates to quality evaluation.

    In context

    AgentOps

    On-going AI agents monitor, evaluate and improve.

    AI Agent Execution Trace

    Recording related steps of an agent run for analysis.

    Questions about this term

    Is logging the same as observability?

    Logs are just a data source.

    What Should Be Traced?

    At least relevant model, tool, handoff, error and runtime events taking into account sensitive data.

    All details and background

    Among other things, model calls, turns, tool calls, handoffs, guardrails, errors, latencies and token consumption can be observed. The aim is not only monitoring, but also causal analysis via connected execution traces.

    ELI5 – Explained simply

    Like a tachograph: later you can see what steps the agent took, what tools it used and where something went wrong.

    Why it matters in practice

    Crucial for debugging, quality, cost control and governance of productive agent workflows.

    Example

    Possible example: A team tracks a failed customer process from the first model call to a handoff to a faulty CRM tool call.

    Pitfalls and limits

    • Log sensitive content unfiltered; observe only final results; store trace data without shared IDs; and do not link costs and error rates to quality evaluation.

    Origin & history

    Agent tracing uses established trace/pan concepts, for example from OpenTelemetry; current SDKs record agent-specific events.

    Use in mid-sized companies

    • Possible applications: production monitoring of support, research and automation agents; error analysis, cost control and quality improvement.

    Related terms

    Matching Flow-alerta service

    AI assistants & automationAI assistants & automation: test a suitable process with clear rights and human test points.

    Matching tool / use case

    View use casesUse Case: Trace-based error and cost analysis; Tools: OpenAI Agents SDK Tracing, OpenTelemetry-compatible observability platforms

    Sources

    Quellenprüfung: 09.10.2026. Anwendungsbeispiele sind mögliche Szenarien, keine behaupteten Kundenfälle.

    Editorially reviewed on:

    Next step

    Put knowledge into practice

    Discuss your projectView use cases