The Architecture That Keeps AI From Authorizing Itself
Design an AI agent authorization architecture that separates proposals, policy, execution, and evidence, including indirect paths that can bypass approval.
AI Governance
The Agent Action Evidence Contract: What Every AI Action Must Record
Define a proposed agent action evidence contract that connects identity, approval, execution, independent observation, and recovery while preserving unresolved outcomes.
The Assurance Independence Model: Six Boundaries for Agentic AI
Apply the proposed Assurance Independence Model to six trust boundaries. Assess shared failures, require evidence, and use mandatory gates before expanding agent authority.
LLM as a Judge: Evaluation Is Not Authorization
Use LLM-as-a-Judge for scoped evaluation without confusing a favorable score with proof or permission. Keep authorization and outcome verification independent.
Who Audits the AI Auditor? Independent AI Assurance
Explore six dimensions of independent AI assurance, with controls that separate agent judgment, authorization, execution and evidence, plus a proposed benchmark.
The Enterprise Architect’s Guide to Surviving the AI Power War
Build an enterprise AI exit strategy with tested model substitution, retained authority, recoverable state, contract rights, and realistic transition costs.
The Agent Control Plane Is the Real Prize in the AI War
Build an agent control plane strategy that separates provider capabilities from business authority, with clear ownership, approval, recovery and exit tests.
Distribution May Defeat Intelligence: Google, Meta, Microsoft and Apple’s Hidden Advantage
Why Google, Meta, Microsoft and Apple can turn devices and everyday apps into an AI distribution advantage, and how enterprises should assess the tradeoffs.
The AI Power Stack: Who Is Winning the AI Race?
Explore the AI power stack, compare leading ecosystems, and see why compute, distribution, agent control, and exit options matter beyond model benchmarks.
AI Feedback Loops: Building Systems That Stay Correctable
Keep AI systems correctable as feedback accumulates. Govern knowledge promotion and withdrawal, protect authorization boundaries, reconcile unknown execution outcomes, and test the conditions that should stop the workflow.
AI Uncertainty: Why Confidence Scores Are Not Enough
Give token entropy, semantic uncertainty, calibration, and abstention distinct jobs. Evaluate how uncertainty changes the next workflow step instead of relying on a single confidence score.
AI-Assisted Decisions: When Repetition Becomes False Confidence
Prevent a tentative AI diagnosis from becoming accepted knowledge through repetition. Preserve source lineage, separate observations from interpretations, and make changed evidence trigger reassessment.
AI Feedback Loops: Cybernetics and Control Theory for Agents
Design agent feedback around goals, observations, bounded actions, verification, and correction. Use control-theory concepts to examine stability, observability, and the limits of automation.
Behaviorism and AI: How Rewards Shape Model Behavior
Understand how rewards and feedback shape AI behavior. Separate reinforcement learning, human preferences, and persistent adaptation, then evaluate whether rewarded behavior actually achieves the intended outcome.
AI Evidence Contracts: Qualify Data and Calibrate Confidence
Build an evidence contract that keeps observations current, scoped, and traceable. Learn why confidence calibration and evidence qualification answer different questions, and how to use both in an operational workflow.
Connectionism in AI: How Neural Networks Learn Relationships
Understand how neural networks learn relationships, then separate model training from retrieval, context, and application memory. Use those distinctions to make clearer enterprise AI architecture decisions.
AI Agent Learning: Governing What Becomes Permanent
Decide which agent observations may become durable memory, procedures, or training data. Build qualification, evaluation, release, and revocation paths that preserve evidence without automatically approving the lesson.
AI Decision Controls: From Learned Patterns to Authorized Actions
Turn model recommendations into bounded, authorized actions. Define execution contracts, recheck approvals, reconcile uncertain outcomes, and test the full workflow before expanding an agent’s production authority.
AI Feedback Is Not Learning: Governing Memory and Model Updates
Separate feedback collection from changes to memory, runbooks, and model weights. Govern each update with provenance, evaluation, release controls, and a practical way to withdraw a mistaken lesson.
AI Agents Should Verify Before They Act
Choose diagnostics that can change the next decision. Bound investigation costs, protect diagnostic access, and keep verified evidence separate from approval, execution, and confirmed service recovery.