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MMARW / INTELLIGENCE / AI

AI, systems, and what comes next

Analysis of artificial intelligence, models, agents, products, and the systems reshaping how work gets done.

AI · Sep 3, 2026 · 5 min read

AI Agents vs Chatbots

A chatbot answers the next message. An agent takes a job and tries to finish it. This piece is the practical split: when a chatbot is enough, when you actually need an agent, and what breaks if you pick wrong.

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AI · Sep 2, 2026 · 6 min read

Keeping the Plan: Causal State Across LLM Calls

Long-horizon agents fail when intent evaporates between calls. This piece covers how to keep a plan stable, distinguish consumed edges from new work, and implement scratchpads, receipts, replay vs replan, and drift evaluation.

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AI · Aug 31, 2026 · 3 min read

The Artifact Standard: Why AI Work Doesn't Count Until It Leaves the Chat

Chat is cheap. A saved document with a URL is the actual unit of work. This short Intelligence piece argues that agents only become useful when they produce inspectable artifacts, and what that means for teams still living in the thread.

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AI · Aug 22, 2026 · 23 min read

What Are AI Agents?

AI agents are goal-directed systems that use AI to execute multi-step tasks through tool use and decision-making.

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AI · Aug 20, 2026 · 12 min read

The Agentic Shift: How AI Orchestration Reshapes Digital Labor

AI agents are moving from isolated assistants toward orchestrated systems that can plan, execute, verify, and hand work across specialized roles. This report examines the operating model, economic logic, and constraints behind that transition.

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AI · Aug 20, 2026 · 29 min read

The AI Productivity Paradox: Why Gains Are Still Elusive

AI investment is accelerating faster than economy-wide productivity. This report examines the installation phase, diffusion barriers, measurement gaps, sector differences, and the conditions that could turn task-level gains into durable economic growth.

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AI · Aug 20, 2026 · 7 min read

Claude Opus 4.8 and the End of the Agent Wrapper Era

The next competitive shift in AI may be less about raw benchmark scores and more about how reliably models can plan, delegate, verify, and admit failure. This essay examines what that shift means for agent wrappers, enterprise workflows, and model economics.

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