October 18, 2026 1 min read Public preview

An Architectural Pattern for Auditable Agentic AI Investigations

ARGUS separates deterministic evidence classification from LLM-driven planning and reporting, creating an auditable boundary around what an agent may conclude.

AIGI EditorialNews Room analysis for AI governance leaders

ARGUS separates deterministic evidence classification from LLM-driven planning and reporting, creating an auditable boundary around what an agent may conclude.

The architecture assigns biological classification to deterministic code. The LLM may select investigative actions and generate narrative, but it cannot alter evidence classifications, verdicts, or state transitions.

Evidence is also typed by provenance. Direct experimental results can support stronger findings; computational motif evidence and regulatory context may inform an investigation but cannot independently confirm or falsify TF binding.

The system makes abstention an explicit outcome. Mixed, underpowered, unavailable, or unresolved evidence is not promoted to a stronger finding.

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