Notifications
Clear all
Topic starter
06/06/2026 2:26 am
TL;DR: Teams ship AI features quickly, but reliable validation still breaks down because probabilistic outputs do not fit deterministic software testing, according to WorkOS’ interview with Braintrust. Continuous evaluation, experiment tracking, and scored datasets are becoming the practical basis for knowing whether prompts, models, and retrieval pipelines are improving or silently regressing.
NHIMG editorial — based on content published by WorkOS: Ameya Bhatawdekar on building AI evaluations at Braintrust
Questions worth separating out
Q: How should security teams implement AI evaluation in production workflows?
A: Security teams should treat AI evaluation as a continuous control, not a pre-launch checklist.
Q: Why do probabilistic AI outputs complicate traditional testing?
A: Probabilistic outputs can produce multiple valid answers for the same input, so exact-match tests miss acceptable variation and still fail to catch drift.
Q: What breaks when AI prompts are changed without evaluation?
A: The system may appear to work in a demo while silently degrading in production.
Practitioner guidance
- Build a reusable evaluation harness Create datasets that reflect the prompts, edge cases, and failure modes most likely to affect production outcomes.
- Version prompt logic like code Store prompt text, scoring rules, and release notes in source control so changes can be reviewed, rolled back, and compared.
- Separate model, prompt, and retrieval signals Track each layer independently during experiment runs so a quality drop can be traced to the right component.
What's in the full article
WorkOS's full article covers the operational detail this post intentionally leaves for the source:
- Interview context from HumanX 2026 in San Francisco, including the practitioner framing around AI evaluation and observability.
- The specific ways Braintrust structures eval datasets, scoring functions, and experiment tracking for development workflows.
- Michael Grinich's questions about prompt engineering as a real engineering discipline, not a side task.
- The article's broader discussion of why continuous evaluation sits alongside development rather than before deployment.
👉 Read WorkOS's interview on AI evaluation and reliable product development →
AI evaluation and observability: are your controls keeping up?
Explore further
06/06/2026 4:06 am
AI evaluation is becoming the missing assurance layer for runtime decision systems. The article is not really about tooling, it is about the fact that production AI behaves like a governed control surface, not a static feature. When outputs vary by context, the assurance model has to shift from pre-release correctness to continuous evidence that behaviour stays within bounds. For identity programmes, that means the confidence gap is no longer just in the model, but in the governance process around it.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
A question worth separating out:
Q: How do teams know if AI observability is actually working?
A: It is working when teams can show which change caused a quality shift, which dataset surfaced the issue, and whether the regression was contained before users were affected. If the team cannot trace behaviour across versions, observability is producing logs, not governance evidence.
👉 Read our full editorial: AI evaluation is becoming core infrastructure for reliable AI products
