AI Workflows

Memory is the boring word that decides whether an AI agent is useful or merely theatrical. A familiar business scene: a team builds an AI workflow to scan documents, generate ideas, produce drafts, and recommend next actions. The demo looks clever. The first week feels magical. Then the cracks appear. The system repeats discarded ideas. It forgets why an option was rejected. It summarizes a project but cannot explain how one failure in March should change a decision in April. Its “memory” is really a longer chat transcript wearing a lab coat. ...

Assembly lines are not exciting because every worker improvises. They are useful because each station does a narrow job, hands the result forward, and leaves as little room as possible for charming chaos. That is also the quiet lesson in A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows.1 The paper looks, at first glance, like another guide to agents, tools, MCP servers, multi-model reasoning, and cloud-native deployment. The tempting summary would be: “Here are nine best practices for building agentic AI.” ...

Captioning looks easy until the caption has to be true. A consumer image captioning model can say, “a man standing at a podium,” and most people will nod. A newsroom cannot stop there. It needs to know whether the man is a senator, a witness, a CEO, a defendant, or simply someone unlucky enough to stand near a microphone. It may need the committee name, the location, the event, the year, the organization behind the banner, and the person half-visible at the edge of the frame. Journalism, as usual, ruins the demo. ...