Agentic AI

Every AI vendor now wants to sell autonomy. Not “software that helps your team,” which sounds quaintly 2023, but agents that plan, act, recover, learn, orchestrate, and perhaps one day replace half the org chart while politely generating meeting notes about it. The problem is not that autonomy is meaningless. The problem is that it is usually measured like a perfume ad: evocative language, dramatic lighting, very little instrumentation. ...

Review is cheap until it has to be correct. That is the uncomfortable lesson behind many agentic AI demos. A system writes an answer. A second model checks it. A third model fixes it. The workflow looks reassuringly managerial, like a tiny consulting firm trapped inside a GPU cluster. But the appearance of oversight is not the same thing as oversight. A weak reviewer can punish a good answer. A weak fixer can damage a nearly correct answer. And if the whole chain receives one final reward, reinforcement learning may end up congratulating the wrong participant. Very corporate, really. ...

A small electronics factory moved from scattered manual quality coordination to an AI-agent-enabled workflow that classifies defects, links them to operational context, and keeps corrective actions under human control.

Peer Review Meets Power Tools: How AI Is Quietly Rewriting Scientific Workflows Research begins with a familiar nuisance: too many papers, too little time, and a creeping suspicion that the most relevant idea is hiding three fields away under someone else’s terminology. Then comes the second nuisance: even after finding the idea, someone must turn it into a hypothesis, a collaborator list, an experiment plan, a protocol, a result, a reviewable claim, and eventually a publishable manuscript. ...

TL;DR for operators Memory is becoming the fashionable upgrade for AI agents: let the system remember past tasks, extract lessons, and improve without retraining the model. Sensible. Also slightly dangerous, in the same way giving a junior analyst a notebook is useful until they start rewriting the notebook after every meeting. The important result is not that memory sometimes contains bad facts. Everyone who has used software, people, or software made by people already knew that. The sharper point is that useful experience can become faulty during the act of consolidation. When an LLM agent compresses raw trajectories into reusable textual lessons, it may strip away conditions, merge unlike cases, or turn a narrow success into a general rule. The memory then looks cleaner while becoming less true. Very enterprise. ...

Zoning is where optimism goes to meet the municipal code. A proposed housing site may look perfect on a dashboard: good transport access, strong demand, reasonable land cost, favourable development projections. Then the real planning work begins. Height restrictions appear. Environmental buffers interfere. Community priorities conflict. A flood-risk layer changes the cost-benefit story. A transport engineer likes the site. A housing officer likes the urgency. A neighbourhood group likes neither the density nor the traffic. The question is no longer “what is likely to happen?” It is “what should be allowed, under which constraints, with what trade-offs, and who can justify that decision in public?” ...

Ask a product manager in a financial technology company a simple question — “How does this feature behave under that framework?” — and the answer may live in five places, three teams, two stale wikis, and one acronym that means different things depending on who had coffee with whom. This is the everyday enemy of enterprise AI. Not lack of models. Not lack of dashboards. Not even lack of documents. The problem is that internal knowledge rarely behaves like a neat public benchmark. It is fragmented, duplicated, partially obsolete, acronym-heavy, and governed by access rules that make the usual “just send it to a cloud assistant” suggestion both naïve and professionally adventurous. ...

A customer support bot does not need a soul. Pleasantly, most airlines have not yet advertised one. But it does need to remember what role it is playing. If it gives policy advice, that advice must remain anchored to the policy. If it apologises for an error, the correction should bind future answers. If the company has told users the assistant is a support agent, the assistant cannot conveniently become a speculative travel blogger, a therapist, a lawyer, or a magic refund machine, depending on which prompt arrives next. ...

Agents have become the new office intern, software engineer, analyst, compliance assistant, and occasional disaster rehearsal all in one. Give one a goal, some tools, a memory store, and permission to act, and it begins to look less like a chatbot and more like a small operating unit. That is the sales pitch. The engineering reality is less tidy. ...

A workflow chart is comforting. It gives everyone boxes, arrows, and the illusion that power follows geometry. In a multi-agent AI system, that illusion fails rather quickly. The agent in the middle of the diagram may not be the one shaping the final answer. The orchestrator may look important because everything passes through it, but another specialist agent may quietly determine the substance. A router may touch only one decision and still decide the entire path. A late-stage formatter may appear humble and yet rewrite the output enough to matter. The org chart lied. Naturally, the workflow diagram learned from management. ...