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18/08/2026 2:41 pm
TL;DR: LLM application explainability depends on execution traces plus evaluation scores, because final-response inspection hides retrieval failures, tool mistakes, and broken intermediate steps, according to Braintrust. For AI teams, the governance problem is not just model interpretation but end-to-end diagnosis across agents, memory, and tool use.
NHIMG editorial — based on content published by Braintrust: Best explainable AI tools for tracing LLM decisions in 2026
Questions worth separating out
Q: How should security teams explain failures in LLM applications and agents?
A: They should use trace-based evaluation, because the failure may originate in retrieval, tool use, memory, or generation rather than in the final response itself.
Q: Why do token attribution methods fall short for agentic AI systems?
A: Token attribution only shows how input text influenced one model call.
Q: How do organisations know if LLM explainability is actually working?
A: They know it is working when the same evidence can diagnose failures in staging and production, and when failed traces reliably reappear as regression cases after changes.
Practitioner guidance
- Instrument end-to-end traces for every agent run Capture retrieval, tool calls, memory lookups, and generation spans so the full execution path is visible before you attempt root-cause analysis.
- Score spans with task-specific evaluators Define scorers for factuality, formatting, policy compliance, and business task completion, then run them on both test cases and production traces.
- Promote low-scoring traces into regression datasets Turn failed traces into reusable test cases so the same error can be rechecked after prompt changes, model updates, or tool changes.
What's in the full article
Braintrust's full article covers the operational detail this post intentionally leaves for the source:
- How the Braintrust SDK and OpenTelemetry instrumentation produce nested traces across model calls, retrieval, and tools
- How custom scorers and LLM-as-a-judge evaluations are configured for production and regression workflows
- How datasets and experiments reuse low-scoring traces as repeatable test cases
- How Braintrust compares with Arize Phoenix, SHAP, LIME, Fiddler AI, and Captum in practice
👉 Read Braintrust's guide to explainable AI tools for LLM applications and agents →
LLM traces and evals: what explainability tools miss?
Explore further
18/08/2026 3:25 pm
Execution-level explainability is becoming an identity problem as much as a model problem. Once an AI system can retrieve context, call tools, and act across steps, governance shifts from asking what the model predicted to asking what the system executed. That makes traceability relevant to agent identity, delegated access, and workload accountability. Teams that treat all AI explainability as model attribution will miss the control boundary where the failure actually occurred.
A question worth separating out:
Q: What is the difference between model attribution and application explainability for AI agents?
A: Model attribution explains how features influenced a prediction in one model call. Application explainability explains the full execution path across retrieval, tools, memory, and generation. For AI agents, the second view is usually more important because the decision emerges from orchestration, not from the model alone.
👉 Read our full editorial: Explainable AI for LLM apps needs traces, not token scores
