Tag: AI Governance
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Agentic AI SDLC: From Prompted to Autonomous, Step-by-Step Guide
If you run software programs for a living, you already know the shape of what is coming: a maturity ladder, gates between rungs, a sign-off before the next rung is funded. That instinct is right. What changes with agentic AI is what the gates are made of. A traditional gate was a meeting, a sign-off,…
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Agentic AI Model Never Decides the Outcome. The Controls Do.
The best agentic AI case studies I have published in the last year share one finding, and it is not about models. Across ten posts covering more than sixty enterprise agentic AI deployments, not one outcome, good or bad, was decided by which model the company picked. Every one was decided by what the agent…
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Anthropic’s AI-Native SDLC Playbook, Mapped to My Agentic AI Playbook
The AI-native SDLC is a rebuilt software development lifecycle in which AI is embedded at every stage — plan, design, build, test, deploy, maintain — and each stage ends by committing a version-controlled artifact that the next stage reads. That chain of artifacts, from intent.md through the spec, the plan, the diff and the incident…
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How Fortune 1000 Companies Organize for Agentic AI
Fortune 1000 companies organize for agentic AI in one of three ways: a centralized AI Center of Excellence, fully embedded teams inside business units, or a hub-and-spoke hybrid that combines both. The published evidence now points clearly at the hybrid. IBM’s study of more than 600 Chief AI Officers across 22 countries found that organizations…
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27 Key Agentic AI Terms, What They Mean and Where They Came From
This is a glossary of 27 key agentic AI terms — what each one means, where it came from, and one authoritative source to read for each. The vocabulary of agentic AI has moved faster than any enterprise glossary I have seen in twenty years of consulting. Terms that were lab jargon eighteen months ago…
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Agentic AI Model Harness: The Layer That Decides Agent Outcomes
What Is an AI Model Harness? An AI model harness is everything wrapped around a raw language model that turns it into a working system — the system prompts, tool interfaces, context and memory management, the agent loop, orchestration, guardrails, permissions, sandboxing, and observability. The model weights are not the harness. And in 2026, the…
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What Agentic AI Actually Costs: The Complete P&L Breakdown
The cost of agentic AI is not the price of a model call. It is every token, tool call, retry, verification pass, escalation, and minute of human review it takes to finish one completed unit of work — and in the twelve months ending December 2025, the price per million tokens fell by roughly half…
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The Coming CxO Power Shakeout Over Agentic AI
A power shakeout is coming in the C-suite over agentic AI: within the next two to three years, CFOs, CMOs, and COOs will move from funding agentic AI deployments to directly managing them, and CIOs and CTOs who keep treating these programs as technology initiatives will find themselves running the plumbing while someone else runs…
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Cost per Completed Task: Agentic AI’s Unit of Account
Cost per completed task is the total spend an agentic AI system incurs to finish one unit of work — every token, tool call, retry, escalation, and minute of human review, added up across the whole loop. Not the price of one model call. The whole loop. This is the thesis I laid out on…
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Two Agentic AI Deployments, One Contained, One Catastrophic
The Difference Was Operational Risk Management Summary: Of the two 2025 agentic AI coding deployments compared here, one sits at Stage 3, Governed, on my Agentic AI Roadmap, and one operated at Stage 2, Piloted, while being marketed as Stage 5, Autonomous. The gap between them was not model capability. It was whether the agent’s…