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20/08/2026 5:22 pm
TL;DR: Agent observability captures tool calls, reasoning steps, memory access, and handoffs so teams can see why an AI agent produced a result, not just whether the request returned successfully, according to Braintrust. The shift matters because silent agent failures now evade traditional APM, making trace-based evaluation and release gates part of operational control rather than optional debugging.
NHIMG editorial — based on content published by Braintrust: Agent observability: The complete guide for 2026
By the numbers:
- The free tier includes 1 GB of processed data and 10k evaluation scores per month.
- Notion increased issue triage from 3 issues per day to 30 by using Braintrust's observability and eval workflows.
Questions worth separating out
Q: How should teams govern AI observability assistants in production?
A: Govern them as privileged machine identities with scoped permissions, strong logging, and explicit approval paths for any action that changes production state.
Q: Why do AI agents create governance problems that normal access reviews miss?
A: AI agents can read, copy, transform, and re-share data after the original access decision, so a static review of entitlements does not capture downstream impact.
Q: What do security teams get wrong about telemetry for AI agents?
A: They often treat telemetry as a troubleshooting tool instead of a governance control.
Practitioner guidance
- Instrument every agent step with typed spans Capture tool name, arguments, model response, state transition, and memory operation for each step so a single run can be reconstructed end to end.
- Propagate trace IDs across agent handoffs Carry the same trace ID through parent and sub-agent runs, and record handoff payloads as first-class spans so upstream context loss is visible.
- Score live traces against production evals Attach online scorers to sampled production traces and convert recurring failures into evaluation cases so regressions become testable before the next release.
What's in the full article
Braintrust's full guide covers the operational detail this post intentionally leaves for the source:
- Framework-specific tracing examples for LangGraph, Mastra, OpenTelemetry, and other agent stacks
- The minimum viable trace schema fields needed to make agent behaviour queryable at scale
- How online scoring turns live traces into evaluation cases that can block future regressions
- Implementation workflow details for using the Braintrust CLI, GitHub Action, and trace store together
👉 Read Braintrust's full guide to agent observability and production trace scoring →
Agent observability and AI agent failures: are your controls keeping up?
Explore further
20/08/2026 6:12 pm
Agent observability is becoming a governance control, not just a debugging feature. Once agents can call tools, retain state, and hand work off across steps, their behaviour becomes an access and accountability problem. Structured traces give security and platform teams a way to reconstruct what the agent did, which is the minimum condition for trust in production agent systems. For practitioners, observability now belongs alongside policy, logging, and review as a control surface.
A question worth separating out:
Q: How do security teams know if agent observability is actually working?
A: Observability is working only when teams can tie together token activity, tool calls, and latency for a specific agent session. If those signals are disconnected, the logs may show traffic but not governance. The test is whether investigators can reconstruct the path of a meaningful agent action from start to finish.
👉 Read our full editorial: Agent observability is becoming the control plane for AI agent quality
