Explainable AI for LLM apps needs traces, not token scores

At a glance

What this is: This guide compares explainable AI tools and finds that LLM applications need trace-based evaluation, not just token attribution or model-level feature explanations.

Why it matters: IAM, NHI, and AI security teams need execution-level visibility because agentic systems can fail through retrieval, tool use, and memory, creating governance gaps that traditional output-only review will miss.

👉 Read Braintrust's guide to explainable AI tools for LLM applications and agents

Context

Explainable AI for LLM applications is fundamentally different from explainability for classical predictive models. When an agent or application makes a bad decision, the cause may sit in retrieval, tool selection, memory, or a later generation step, so reviewing only the final output hides the control failure.

That matters for AI governance, identity security, and non-human identity oversight because agents increasingly act through tool access and delegated context. Tracing and evaluation together provide the evidence chain needed to see where a multi-step run failed, which is closer to how teams should govern agentic systems.

For teams already thinking about agent identity and workload identity, this is the same governance pattern seen in other execution-heavy systems: you need to know what the system did, what it was allowed to do, and where the failure occurred. That need is typical for LLM applications and agents, not an edge case.

Key questions

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. A useful explainability process records each step, scores each span against task criteria, and turns failed runs into regression tests. That approach gives teams repeatable evidence and a clearer governance trail.

Q: Why do token attribution methods fall short for agentic AI systems?

A: Token attribution only shows how input text influenced one model call. Agentic systems create outcomes across multiple steps, so the error may happen earlier in retrieval or tool selection and only appear later in the output. Without traces, teams can see the symptom but not the step that caused it.

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. The signal is not just visibility, but whether the organisation can repeat, score, and compare the same workflow over time.

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.

Technical breakdown

Why token attribution does not explain multi-step LLM failures

Token attribution estimates how much individual input tokens influenced a single model output. That is useful when the question is narrow, but it does not explain a larger application path. In LLM systems, the wrong answer may begin with a bad retrieval result, continue through a poor tool argument, and only surface at the final generation step. A token map of the answer cannot reconstruct that chain. Trace-level explainability captures the sequence of spans across retrieval, tool use, memory, and generation, so evaluators can identify the first failing step instead of the last visible symptom.

Practical implication: teams should instrument multi-step traces before they try to judge model quality.

How traces and evaluators turn debugging into governance

A trace is only half the picture unless it is paired with evaluation. In application-level explainability, each span can be scored for accuracy, format compliance, usefulness, or task-specific criteria. That score tells a team whether the system merely produced a response or actually completed the task correctly. When those scores are attached to datasets and regression runs, the organisation gets repeatable evidence across staging and production. This turns explanation into an operational control rather than a one-off debugging exercise, which is why trace-based evaluation matters for LLM governance.

Practical implication: connect evaluation scoring to regression testing so failures become repeatable control signals.

Where classical feature attribution still fits in 2026

Feature attribution remains relevant for tabular, text, and vision models, especially where the team needs to understand how inputs influenced a single prediction. SHAP estimates feature contribution using Shapley values, while LIME approximates local behaviour around one sample with a surrogate model. Captum extends attribution for PyTorch models at the feature, layer, neuron, and concept level. These methods explain model behaviour, but they do not explain orchestration, memory, or tool execution in agentic systems. That is why they complement trace-based explainability rather than replace it.

Practical implication: use attribution for model predictions and traces for LLM application paths.

NHI Mgmt Group analysis

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.

Trace plus eval is the right control pattern for agentic systems. A trace shows the path, but only an evaluation score tells you whether each step met the intended standard. That pairing creates a reusable evidence chain for staging, production, and regression testing. For practitioners, the key conclusion is that explainability should be wired into operational assurance, not left as an after-the-fact investigation tool.

Braintrust's real analytical signal is the workflow pattern, not the branding. The article shows a broader market shift toward closed-loop evaluation, where traces feed datasets and datasets feed regression tests. That pattern is increasingly relevant for AI governance programmes that need repeatable diagnostics across multi-step systems. Practitioners should focus on whether their tooling can preserve evidence from run to run, not just display a trace once.

Classical explainability still matters, but it no longer covers the dominant risk in agentic AI. SHAP, LIME, and Captum remain useful for model-local interpretation, especially in structured and vision workloads. But the moment a system delegates work across retrieval and tools, the main question becomes execution integrity. For identity and AI governance teams, that means the emerging control plane is about traceable action, not only interpretable prediction.

What this signals

Execution tracing is becoming a governance boundary for AI programmes. If teams cannot connect a response back to the retrieval, tools, and memory that produced it, they cannot prove whether the agent stayed within scope. That is why explainability should sit alongside access control and audit evidence, not only model QA.

The practical shift is toward closed-loop assurance, where traces become test cases and test cases become regression controls. That makes AI observability and explainability part of the same operational fabric, especially when agents are tied to sensitive data or delegated privileges.

AI agent governance is still lagging adoption: according to our AI Agents: The New Attack Surface report, 92% see governance as critical but only 44% have policies in place. For practitioners, that gap means explainability tooling will be judged on evidence retention and auditability, not visual polish.

For practitioners

• 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.
• Separate model attribution from application diagnosis Use SHAP, LIME, or Captum for classical model interpretation, but keep trace-based evaluation as the primary diagnostic method for LLM applications and agents.
• Align agent governance with workload accountability Map each agentic workflow to the identities, permissions, and data sources it can touch, then confirm the trace proves those boundaries were respected.

Key takeaways

• LLM explainability fails when teams look only at the final answer instead of the execution path that produced it.
• Trace-based evaluation is the most practical control pattern for diagnosing agentic AI because it links steps, scores, and regression evidence.
• Classical attribution still has value, but it does not replace trace-level governance for multi-step AI applications and agents.

Key terms

• Trace-based Explainability: An approach to AI explainability that reconstructs how an application reached an output by examining the full execution path. It captures retrieval, tool calls, memory, and generation so teams can diagnose the step that introduced an error, not just the final response.
• Span-Level Evaluation: A method of scoring individual steps inside a larger AI workflow. Each span can be measured for accuracy, compliance, or task quality, allowing teams to see which part of an agent run succeeded or failed and to reuse those results in testing and monitoring.
• Local Explainability: Local explainability describes why a model produced one specific result for one specific case. It is most useful when a customer, investigator, or reviewer needs a decision reason that is tied to the exact inputs in play, such as a credit denial or a fraud alert.
• Feature Attribution: A family of methods that estimate how much each input contributed to a model prediction. It is well suited to classical tabular or vision models, but it does not explain multi-step orchestration, tool execution, or memory effects in agentic systems.

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

👉 Braintrust's full article breaks down trace scoring, regression workflows, and the tool comparison in detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps security practitioners translate identity controls into operational assurance for modern digital systems.