Connects AI-enabled capabilities to operational objectives, aligning with an AI governance framework, adhering to risk thresholds, and complying with policy constraints (e.g., EO 13960, NIST AI RMF) to ensure the development of trustable systems within a robust cognitive architecture.
Maps AI agents, LLMs, inference pipelines, and autonomous systems into workflows, establishing a clear AI governance framework that ensures effective human-AI handoffs and escalation paths within a cognitive architecture, ultimately leading to the development of trustable systems.
This approach expands traditional architecture views to encompass an AI governance framework, integrating cognitive architecture, prompt interfaces, vector databases, model registries, and trustable systems with effective trust scoring systems.
Implements an AI governance framework that integrates model drift detection, prompt validation, telemetry logging, feedback loops, and alignment auditing within a robust cognitive architecture to ensure the development of trustable systems.