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As artificial intelligence evolves beyond traditional automation and generative capabilities, ensuring its reliability, transparency, and ethical integrity becomes a significant challenge. In this session, Jonathon Wright introduces AI-Assurance, a new approach for validating and governing Agentic AI systems. These systems use Computer Use Automation (CUA), Chain of Thought (CoT), and GraphRAG architectures to improve model-driven decision-making, self-learning, and adaptive agent-based CUA execution. Building on decades of advancements in cognitive engineering, AI, and automation, this talk explores how large action models (LAM), large vision models (LLaVA), and AI-driven software agents are redefining productivity and assurance across the software development lifecycle (SDLC). Attendees will gain insights into the evolution of building AI-infused system methodologies, the shift from traditional process automation to autonomous AI agents, and the role of governance models like ISO 42001 and AI risk compliance frameworks (AI TRiSM / ISO / NIST). Key topics include: Agentic AI and the Future of AI-Augmented Workflows: How AI systems transition from passive assistance to active moral decision-making. GraphRAG and Chain of Thought Reasoning: How structured AI reasoning enhances software quality, transparency, and compliance. CUA and Large Action Models: The rise of autonomous, neuro-symbolic AI agents for real-time computer use execution. AI-Assurance in the Age of Regulation: Navigating compliance, ethical AI governance, and risk management frameworks. This session is essential for AI practitioners, modern software developers, and business decision-makers looking to integrate AI-Assurance governance into their AI-productivity or AI-workforce workflows. It also helps in avoiding bans from emerging AI liability laws and complying with the EU AI Act.