Researchers propose Cognitive Core, a governed AI architecture designed for high-stakes institutional decisions that achieves 91% accuracy on prior authorization appeals while eliminating silent errors—a critical failure mode where AI systems make incorrect determinations without human review. The framework introduces 'governability' as a primary evaluation metric alongside accuracy, demonstrating that institutional AI requires fundamentally different design principles than general-purpose agents.
The emergence of Cognitive Core addresses a fundamental gap in AI deployment: general-purpose agent frameworks operate through conversational inference and post-hoc accountability reconstruction, creating systemic blind spots in institutional contexts where errors carry regulatory and human consequences. This research identifies and quantifies 'silent errors'—the most dangerous failure mode in compliance systems—where incorrect determinations execute without triggering review signals. The 91% accuracy rate alone would be noteworthy, but the zero silent errors achievement represents a qualitative breakthrough in institutional AI reliability.
The governance architecture reflects lessons learned from decades of compliance failures in financial and healthcare systems. By making human review a condition of execution rather than an optional post-hoc check, and embedding SHA-256 hash-chain auditing directly into computation, Cognitive Core transforms governance from an external wrapper into a core architectural principle. The framework's nine typed cognitive primitives (retrieve, classify, investigate, verify, challenge, reflect, deliberate, govern, generate) provide granular control points where human intervention becomes deterministic rather than heuristic.
For enterprise adoption, the configuration-driven domain model dramatically reduces deployment friction—new institutional decision domains require YAML configuration rather than engineering resources. This modularity accelerates time-to-compliance for regulated industries facing prior authorization, clinical triage, and regulatory determination backlogs. The baseline comparison against ReAct and Plan-and-Solve (implemented as realistic prompt-based deployments) establishes that governance frameworks outperform conversational agent approaches by measurable margins in institutional contexts where silent errors carry compliance liability.
- →Cognitive Core achieves 91% accuracy on prior authorization appeals while eliminating silent errors entirely, versus 55-45% accuracy with 5-6 silent errors in baseline systems.
- →Governed AI architecture treats human review as a mandatory execution condition rather than optional post-hoc validation, fundamentally changing institutional risk profiles.
- →Governability emerges as a critical evaluation metric alongside accuracy for institutional AI systems, measuring how reliably systems know when autonomous action is inappropriate.
- →Configuration-driven domain modeling enables rapid deployment to new institutional decision domains without custom engineering, reducing compliance implementation costs.
- →Hash-chain audit ledgers endogenous to computation provide tamper-evident governance trails, addressing regulatory and liability requirements in high-stakes decision contexts.