Agentic AI

Approval meetings exist for a reason. An analyst proposes an investment. Legal identifies a compliance problem. Operations notices that the promised delivery date is fictional. Someone with decision authority compares the evidence, resolves what can be resolved, and escalates what cannot. Now remove that final decision-maker. Give every participant access to APIs, databases, payment systems, and customer communications. Allow them to act autonomously. Then ask the same participant who proposed the decision to explain why it was sensible. ...

Approval is boring. That is why most automation diagrams hide it. A user request arrives, a sensor emits a signal, an AI agent reasons through the situation, a tool call fires, and something in the real world changes. A stock level is replenished. A traffic light is adjusted. A healthcare alert is escalated. In the clean version of the diagram, the agent looks wonderfully autonomous. In the operational version, someone eventually asks the unpleasant question: who allowed this thing to act? ...

Groceries are where economic theory goes to become annoying. A household may have a budget, a doctor’s warning about sodium, a child who refuses vegetables with the confidence of a trade negotiator, a cultural preference, a supermarket promotion, and a sudden chicken price increase. Most apps touch only one piece of this mess. Budgeting apps tell you where the money went. Nutrition apps tell you what you should have eaten. Shopping apps tell you what is on sale. Very helpful, provided your life is already organized into clean software categories. ...

A robot does not need evil intent to become dangerous. It only needs a bad next action. “Turn on the microwave” sounds ordinary until the microwave contains a fork. “Pick up the knife” may be harmless in a cooking task until the next move is to swing it around. “Turn on the stove” may be safe for one step and unsafe three steps later if the agent forgets to turn it off. Physical risk is annoyingly literal that way. It does not wait for a model to finish reflecting on its values. ...

A farmer does not need ninety-three charts before deciding what to do next. That sounds obvious. Unfortunately, “obvious” is where many agentic AI workflows go to die. Give an LLM a model explanation, ask it to improve the explanation, let it generate more analysis, feed the results back, and repeat. The process feels responsible. More checks. More plots. More reasoning. More “depth.” Somewhere in the background, a product manager begins to hear the soft music of enterprise automation. ...

Beam planning sounds like the sort of work automation should have solved years ago. There is a target. There are organs at risk. There are dose constraints. There is an optimizer. Surely the machine should find the best plan while humans do something more dignified than nudging parameters inside a treatment planning system for the seventeenth time. ...

The problem is not that RL cannot plan. It is that it keeps learning the wrong object. A warehouse robot can learn to pick up box A from shelf B and move it to station C. Very impressive, until tomorrow’s warehouse has different boxes, different shelves, and a new station name. The action label changed. The task structure did not. ...

Losing is not the problem. Being seen losing is. Put two AI agents in the same workflow and the design immediately stops being a simple productivity question. One agent writes code. Another reviews it. A third ranks alternatives. A fourth routes the next task to whoever looks most competent. At the slide-deck level, this is “multi-agent collaboration.” In the logs, it is often a scoreboard with better manners. ...

A regional delivery company moved from human-coordination-heavy breakdown response to an AI-agent-enabled fleet workflow that links driver logs, inspections, fuel data, and repair records into governed maintenance actions.

When Tools Think Before Tokens: What TxAgent Teaches Us About Safe Agentic AI Tools are supposed to make AI safer. That is the sales pitch, anyway. Give the model access to curated biomedical databases, let it call APIs instead of hallucinating from memory, and clinical reasoning suddenly becomes more grounded. Less improvisation, more evidence. Less theatrical confidence, more traceable work. ...