Bounded AI Control Loops for Hybrid Cloud Operations
Design bounded AI control loops for hybrid operations. Connect observation, policy, scoped execution, recovery, and cost limits through a practical autonomy ladder and accountable owners.
AI agents
A New Model Is a New Authority Envelope: Requalifying AI Agents After Model Releases
A model upgrade can change what an AI agent does with existing permissions. Requalify its action boundaries, compare tool behavior, test independent controls, and expand production autonomy only when the evidence supports it.
Restoring AI Agent Autonomy After an Incident
Restore agent authority through action-level requalification, measured canary exposure, and enforceable promotion and demotion rules. Use current evidence to decide which autonomy levels can safely return.
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.
Rehearse Before You Automate: Why AI Agents Should Practice Before They Act
Rehearse agent behavior in controlled environments before granting production authority. Test realistic failures, policy conflicts, recovery, and repeated trials against the scope the agent will actually receive.
The Agent Sprawl Crisis: How CIOs Should Register, Consolidate, and Retire Digital Workers
TL;DR Sanctioned AI agents can create the same portfolio problems as shadow technology: duplicated capabilities, conflicting actions, unmanaged dependencies, excessive token spend, orphaned identities, and operational debt. The answer is not to stop business units from building. It is to require every agent to enter a governed lifecycle that begins before deployment and ends with … Explore: The Agent Sprawl Crisis: How CIOs Should Register,…
AI Agents Are the New Control Plane: Governing Identity, Tool Access, and Observability Across Azure, AWS, Google Cloud, and VCF
Introduction The first article in this series focused on multicloud control-plane sprawl. Azure, AWS, Google Cloud, and VMware Cloud Foundation each bring their own identity model, policy engine, network architecture, observability stack, automation surface, and lifecycle model. That is already enough to create governance fragmentation. AI agents add another layer. An agent is not just … Explore: AI Agents Are the New Control Plane: Governing…
How to Build an AI Agent from Scratch with CrewAI and Clarifai
Introduction AI agents are reshaping automation by moving from simple scripts to adaptive, intelligent systems that can plan, reason, and act on real goals. Unlike traditional software, these agents can gather data, analyze information, break down objectives, and execute steps, all with a surprising degree of autonomy and flexibility. Today, you will learn how to … Explore: How to Build an AI Agent from Scratch…
The Practical Roadmap to Becoming a Generative AI Engineer
Introduction The generative AI revolution is redefining what artificial intelligence can achieve in practical, everyday settings. What began as experimental projects around models like GPT-3 has rapidly evolved into a critical component of modern business and technology. Today, companies across industries are leveraging AI for tasks such as text generation, visual content creation, and automated … Explore: The Practical Roadmap to Becoming a Generative AI…
15 Essential GitHub Repositories for Mastering AI Agents and MCP Integration
1. Learn AI and LLMs from Scratch Repo: ashishps1/learn-ai-engineeringThis structured curriculum is designed for beginners and those reviewing AI basics. It includes free guides and resources for mastering artificial intelligence and large language models from the fundamentals up. 2. Microsoft AI Agents for Beginners Repo: microsoft/ai-agents-for-beginnersStart with 11 clear, practical lessons. This repository helps you … Explore: 15 Essential GitHub Repositories for Mastering AI Agents…