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19/08/2026 4:07 pm
TL;DR: Logs capture discrete events while traces connect the full request path, which is essential when LLM and agent workflows branch across retrieval, model calls, and tools, according to Braintrust. The practical lesson is that request-level observability, not event-level noise, determines whether teams can debug, evaluate, and improve production AI behaviour.
NHIMG editorial — based on content published by Braintrust: Tracing vs logging for LLM apps: what's the difference and when to use each
Questions worth separating out
Q: How should teams instrument LLM apps so logs and traces work together?
A: Teams should keep structured logs for event detail, but every log record must be attached to the request trace through shared identifiers.
Q: Why do traces matter more than logs for debugging AI agents?
A: Traces matter because AI agents do not always follow the same path twice.
Q: What do organisations get wrong about AI observability?
A: They often confuse technical telemetry with governance evidence.
Practitioner guidance
- Link logs to trace IDs by default Require every structured log emitted by an LLM or agent service to carry the same trace and span identifiers as the request it belongs to.
- Instrument each AI step as a span Break the workflow into retrieval, model call, tool call, and post-processing spans so timing, inputs, outputs, and metadata are visible at the step level.
- Retain request context for governance reviews Keep the trace record long enough to support incident investigation, access review, and quality scoring.
What's in the full article
Braintrust's full article covers the operational detail this post intentionally leaves for the source:
- How the platform represents traces, spans, and structured log events in one workflow
- The exact fields attached to retrieval, model, and tool spans for debugging and evaluation
- How OpenTelemetry links log records to trace identifiers in practice
- Examples of scoring traced production behaviour for retrieval quality, answer quality, and task completion
👉 Read Braintrust's explanation of tracing vs logging for LLM apps →
Tracing vs logging in LLM apps: which one should teams use?
Explore further
19/08/2026 4:40 pm
Tracing is becoming the audit layer for AI workflows. When LLM systems retrieve data, call tools, and hand off between services, a flat log stream is no longer enough to explain who or what caused an outcome. The missing piece is request context, and that is the same governance problem identity teams face when access is delegated across systems. Practitioners should treat traceability as a control requirement, not an engineering preference.
A question worth separating out:
Q: How do logs and traces support access review for AI workflows?
A: They create evidence about which request touched which data, tool, or downstream system. That is important when AI services operate with delegated access, because reviewers need the surrounding context, not just a timestamped event, to understand whether the action was expected and appropriately scoped.
👉 Read our full editorial: Tracing vs logging for LLM apps: why request context matters
