Category: Agentic AI Governance
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What Is Agentic AI Context Compaction? Decide What Goes
Agentic AI context compaction is the step in an agent’s runtime loop where the harness takes a conversation that is nearing the context window limit, has the model write a summary of it, and restarts the loop with that summary in place of the original history. Everything before the summary is gone from the model’s…
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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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What Is an Agentic AI Eval Suite? A Gate, Not a Demo
An agentic AI eval suite is a fixed bank of tasks, each with a known-good answer and a grader, that you run against the whole agent, model plus harness plus tools, every time anything changes, and whose pass rate decides whether the change ships. The demo shows you one run on one happy path. The…
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What Is Agentic AI SKILL.md and Why It Is Advisory and Cannot Be Enforced
An agentic AI skill is a folder of instructions, reference files and scripts that an agent loads on demand when a task matches the skill’s description. Anthropic published the format in October 2025 as a SKILL.md file with YAML metadata on top and plain Markdown instructions below. By December it was an open standard. Other…
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Agile in Agentic SDLC? Keep the Ceremony. Retire the Artifact.
An agentic SDLC is a software development lifecycle in which AI agents complete whole units of work under enforced boundaries, and it does not retire agile. It retires agile’s artifacts. Every ceremony you run still has a reason to exist. What each one produces has to stop being a document a human interprets and start…
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10 SDLC Assumptions Agentic AI Breaks, Mapped
Agentic AI architecture is not the software development lifecycle with a model bolted onto it. It is a different lifecycle, because the thing being shipped no longer behaves the same way twice. The traditional SDLC assumes you write a specification, test outputs against expected values, and promote a build that behaves in production the way…
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What Is Agentic AI Architecture? The Six Layers That Actually Decide Outcomes
Agentic AI architecture is the design of the system around the model: the layers that decide what an agent is asked to do, what it may reach, what it is forbidden to do, how far it may act without a person, and how any of that is proved afterwards. The model is a component in…
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What Is an AI-Native SDLC? Intent Becomes the Unit of Work
An AI-native SDLC is a software development lifecycle in which the unit of work is committed intent rather than an assigned instruction, and each stage ends by writing a version-controlled artifact that the next stage reads. Anthropic published the clearest description of one on August 21, 2026 in The AI-Native SDLC playbook, and has since…
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10 Facts You Wish You Knew Before Building Agentic AI Solution
Most agentic AI projects fail on facts that were knowable before anyone wrote code. Here are the ten I most wish every architect and CIO knew going in. None of them are about how smart the model is. Every one is a property of the model you rent; every fix is a property of the…
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Agentic AI Governance: Managing the Harness Across the Five-Stage Roadmap
What Agentic AI Governance Actually Governs Agentic AI governance is the discipline of controlling what an AI agent is allowed to do, verifying what it actually did, and proving both to an auditor — and in 2026 the object of that governance is no longer the model. It is the harness. The industry has converged…