Topic starter
20/08/2026 6:10 pm
TL;DR: AI quality improves when teams turn production traces into datasets, then use code-based scorers for deterministic checks and LLM-as-a-judge scorers for nuanced outputs, according to Braintrust. The core shift is from shipping AI on vibes to treating evaluation as infrastructure, because agent failures often only appear when tool use, retrieval, or synthesis breaks in production.
Editorial analysis by NHI Mgmt Group, based on content published by Braintrust: “How to evaluate LLMs and AI agents in production: The Braintrust way”.
Key questions
Q: What breaks when client-facing AI is only tested with single prompts?
A: Single-prompt tests miss behavioural drift, which is when a model stays compliant at the start of a conversation but becomes unsafe after several exchanges.
Q: Why do trace-based evals reduce risk in AI and agent programmes?
A: They reduce risk because they make quality measurable before deployment and let teams replay the exact failure path that produced a bad result.
Q: How do security teams know whether an agent evaluation process is working?
A: Look for evidence that evaluation results change decisions.
Practitioner guidance
- Build production traces into test datasets Capture real agent interactions, tag failures, and promote those traces into repeatable eval cases so regressions are tested against actual workflows.
- Separate deterministic and subjective scoring Use code-based scorers for format, schema, and factual checks, then use LLM-as-a-judge scorers for tone, coherence, and other nuanced qualities.
- Test both end-to-end and step-level behaviour Measure the final outcome and each intermediate span, including tool selection, argument construction, retrieval relevance, and synthesis quality.
Bottom line: Production AI testing needs to move from prompt-only checks to trace-based evaluation if teams want to catch regressions before users do.
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20/08/2026 6:46 pm
Trace-driven evaluation is becoming the control plane for production AI quality. Traditional software testing assumes deterministic behaviour, but LLMs and agents can fail in ways that only become visible after deployment. Turning production traces into datasets gives teams a way to measure behaviour where it actually occurs, which is why evaluation should be treated as infrastructure rather than a last-stage QA exercise.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: What is the difference between evaluating the final answer and evaluating agent steps?
A: Final-answer evaluation checks whether the outcome looks acceptable, while step evaluation checks whether the agent chose the right tool, passed the right arguments, and used the right context along the way. Both are necessary because a plausible final response can hide a bad internal decision. Step-level evaluation is what reveals where the workflow actually went wrong.
👉 Read our full editorial: Evaluating LLMs and AI agents in production needs trace-based tests