From Black Box to Trusted Systems: Explainable AI for Enterprise and Government with 3A Agents
D. R. Sara argues that explainability for consequential AI has to cover more than a model output. He presents the 3A Agent framework for preserving context, reasoning, policy checks, actions, and outcomes across enterprise and government decision workflows with human review.
In D. R. Sara's framework, trusted AI depends on the record around a decision, not only on an explanation of the model. The 3A Agent architecture connects five operational layers: context and source provenance, reasoning, policy controls, action logging, and monitoring. Sara explains how those layers can support bounded autonomy and meaningful human review in enterprise and government workflows. He also distinguishes a governed decision record from a plausible after-the-fact rationale, then outlines an adoption path that starts with high-consequence workflows and expands autonomy only as controls and evidence mature.
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