Observability
What happened during the run?
Reference notes by Min · Reviewed October 12, 2026
Definition
Observability uses emitted signals to understand a running system’s behavior. Agent workflows benefit from correlated traces, metrics and logs across tool calls.
Observability helps operators understand what a system is doing by connecting its emitted signals to meaningful questions. For agents, a request may cross models, tools and services; isolated logs can leave the sequence difficult to reconstruct.
What it covers
- Traces
- A linked path through a task’s operations.
- Metrics
- Rates, latency, errors and usage.
- Logs
- Events with useful operational context.
Choose operational questions first: why did a task stop, which tool consumed the budget, and where did an unauthorized operation originate? Collect signals that answer those questions without indiscriminately retaining every prompt and response.
In practice
Practical design patterns by Min.
- Correlate task, agent and tool-call identifiers.
- Monitor denials, retries, cost and unusual call patterns.
- Redact secrets and minimize sensitive payload capture.
Carry a task identifier through model calls, authorization checks and downstream operations. Correlate it with trace and span identifiers where supported.
Track latency, errors, retries, cost, policy denials and completion outcomes. Link an alert to an actionable owner and investigation view.
Research notes
Source-grounded details and editorial applications. Follow each link for the original document.
Connect a task across services
OpenTelemetry describes a distributed trace as a request path made of spans. For an agent workflow, carrying a task or run identifier across model calls, policy checks and tools is an implementation pattern that lets an operator reconstruct where time, retries and failures occurred.
Use logs with context
A timestamped log message may not identify the request or operation that produced it. OpenTelemetry explains that logs become more useful when associated with a span or correlated with a trace. Capture only the attributes needed to answer operating questions, and keep sensitive prompt content out of routine telemetry when possible.
Working example
Investigating repeated retries
An agent becomes slow and expensive.
Illustrative scenario, not a reported deployment.Why the controls matter
A delayed support task might show a successful model response followed by repeated tool timeouts. The combined view identifies the integration issue instead of attributing every failure to the model.
Common pitfalls
- Collecting everything without a clear question.
- Equating telemetry with a complete audit record.
Raw telemetry can expose customer content or secrets. Use controlled attributes, redaction and retention rules, and document when sampling may omit evidence.
Questions for operational review
Use these questions to decide what evidence your team needs. They are editorial prompts, not a compliance checklist.
- Can an operator follow one task end to end?
- Does each alert identify an actionable problem?
- What information is excluded or sampled?
Articles linking here
References
Primary documents reviewed for this entry. Examples and implementation patterns are editorial synthesis.
- OpenTelemetry: Observability primer ↗Accessed October 12, 2026