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Explainability & Attribution Agents

Provide citations, source tracebacks, or reasoning chains for generated content.

Description

Produces the chain from output back to source: which passages informed which statements, which prompt segments drove which behaviour, what intermediate reasoning was involved. The difference between a system that asserts and one that shows its working.

When it fits

Regulated decisions, audit contexts, and anywhere a person must defend the output to a third party rather than merely accept it.

When it does not fit

Low-stakes assistance where attribution overhead exceeds the value of traceability.

Governance requirement

Attribution must be verifiable, not decorative. A citation the reader cannot follow to a real, retrievable source is worse than none, because it manufactures confidence.

Characteristic failure

Post-hoc rationalisation presented as attribution. A model asked to explain its answer will generate a plausible explanation whether or not it reflects how the answer was produced — and this is genuinely difficult to distinguish from real feature attribution.

Example

An auditor following a machine-generated explanation of a balance movement back through the specific journal entries and match records that produced it, and finding each one.

AI solution components10
  • Token-Level Attention Visualizer
  • Causal Attribution Graph Generator
  • Prompt Attribution Analyzer
  • Citation Chain Tracer
  • Generation Reason Extractor
  • Traceable Decoding Path Logger
  • Saliency-Based Summary Annotator
  • Layer Influence Comparator
  • Factual vs Generative Attribution Classifier
  • Visual Attribution Interface Builder
Agent pattern solutions1