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20/08/2026 5:53 pm
TL;DR: Production observability for AI agents still misses failure modes that teams did not define in advance, allowing stale context, repeated tool retries, and unresolved requests to slip through dashboards, according to Braintrust. The practical shift is from monitoring known defects to clustering traces, confirming patterns, and turning them into scorers, datasets, and review workflows before users surface the problem.
NHIMG editorial — based on content published by Braintrust: How to discover hidden failure patterns in your AI agent's production traffic (2026)
By the numbers:
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
- Only 5.7% of organisations have full visibility into their service accounts.
Questions worth separating out
Q: What breaks when AI agent failures are only tracked with predefined scorers?
A: Teams miss behaviours they never described in advance, including stale retrieved context, repeated retries, and unresolved workflows.
Q: Why do local AI agents complicate identity and access management?
A: They can retain legitimate permissions while changing timing, prioritisation, and action sequence outside human presence.
Q: How do security teams know whether a hidden agent failure is real?
A: Validate the cluster at trace level, measure how much traffic it represents, and check whether the pattern correlates with user frustration or unresolved requests.
Practitioner guidance
- Build a cluster-review loop for production traces Group agent traces by behaviour, open the unusual clusters manually, and confirm whether the label matches the underlying conversations before promoting anything into policy or scoring.
- Convert confirmed failures into deterministic scorers When a cluster has a stable label, turn it into a scorer that flags future traces with the same pattern.
- Add human review for subtle or high-impact clusters Route clusters that are low-volume, ambiguous, or user-visible into reviewer queues before automation hardens the wrong behaviour.
What's in the full article
Braintrust's full research covers the operational detail this post intentionally leaves for the source:
- Daily pipeline mechanics for clustering traces into topics and facet summaries
- Example scorer code and LLM-as-a-judge prompt structure for confirmed failure patterns
- Custom facet design for tracking product-specific failure dimensions such as citation quality and tool reliability
- Trace review workflows for moving clusters into datasets or human assignment
👉 Read Braintrust's guide to discovering hidden failure patterns in AI agent production traffic →
AI agent observability gaps: what hidden failure patterns reveal?
Explore further
20/08/2026 6:31 pm
Hidden failure discovery is now a governance control, not just an observability feature. If teams only score the failures they can name before release, they will miss the behaviour that customers actually experience in production. The practical lesson is to treat trace clustering, cluster review, and scorer creation as part of the control stack, not as optional analytics.
A question worth separating out:
Q: Who should own AI agent failure discovery and response?
A: Ownership should sit across AI engineering, security, and identity governance. Engineering can confirm the behaviour, security can assess risk and impact, and identity teams can verify whether access scope, secrets, or tool permissions enabled the failure. The control gap is shared, so the accountability model must be shared too.
👉 Read our full editorial: Hidden failure patterns in AI agent traffic expose observability gaps
