AI Agent Disaster Recovery: Restore Trust Before Authority
Restore an AI agent’s accepted behavior and current controls before restoring authority. Quarantine suspect memory, requalify evaluators, preserve evidence, and reconcile external actions.
AI memory
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.
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 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 Memory Architecture: Context, RAG, and Persistent State
Choose the right mechanism for AI context, retrieval, persistent records, and workflow state. Design provenance, permitted use, correction, and revocation across both the memory write and read paths.
Retiring Enterprise AI Safely: Decommissioning Models, Agents, Data, and Endpoints
Retire an AI service across its endpoints, identities, data, delegated work, and recovery automation. Verify that it cannot keep acting or silently return through an overlooked deployment path.
Enterprise AI Incident Response: A Runbook for Containing Compromised Agents
Contain compromised AI agents across identity, tools, data, memory, and downstream systems. Reconcile accepted actions, preserve evidence, and use explicit gates for a controlled return to service.
Feedback Is the Control Plane: Preventing AI Systems From Repeating the Wrong Behavior
Decide which production experiences may influence future AI behavior. Qualify observations, route corrections to the right layer, evaluate changes, and control their promotion into durable knowledge or policy.
AI Does Not Have a Mindset: What Repetition Actually Changes in an AI System
Identify what actually changes when an AI system behaves differently. Separate model parameters, instructions, retrieval, memory, tools, and feedback, then govern each layer according to its persistence and authority.