Agentic AI

Demo days are generous. A sales engineer opens a prepared workflow, the agent clicks through a familiar sequence, the dashboard turns green, and everyone politely pretends not to notice how much of the intelligence was smuggled into the setup. ARC-AGI-3 is less polite. The paper introduces an interactive benchmark for agentic intelligence: not a static puzzle, not a multiple-choice exam, and not a coding task with a unit test waiting like a benevolent parent. An agent enters a novel, abstract, turn-based environment. It receives no explicit objective. It must explore, infer the rules, identify what counts as success, build a working model of the environment, and execute a plan efficiently.1 ...

A procurement workflow looks boring until an AI agent touches it. Before that moment, the process is usually wrapped in the comforting machinery of enterprise software: approval rules, validation checks, role permissions, exception paths, and enough audit trails to make everyone feel governed. Then someone inserts an agent and asks it to “handle the workflow.” The agent may know the words. It may call the right tools. It may even produce the next step that looks plausible. ...

Memory is supposed to be the practical part of an AI system. A model answers badly, the system records what happened, and next time the agent avoids the same trap. Neat. Sensible. Almost managerial. Then the organization does what organizations always do: it adds more people. In AI terms, that means more agents, more models, more task routes, more specialized components, and more silent assumptions about who should learn from whom. A small model handles routine work. A larger model handles hard reasoning. A coding model writes scripts. A tool-using agent interacts with apps. Suddenly, “memory” is no longer a notebook. It is institutional infrastructure. ...

Hospital data does not politely arrive as a paragraph. It arrives as an ECG trace, an ultrasound video, a CMR sequence, a physician report, a half-remembered prior diagnosis, and a clinician trying to decide what matters before the next patient enters the room. The popular fantasy of medical AI is that a general model will simply “look at everything” and reason like a specialist. Nice fantasy. Very convenient for demo videos. Less convenient for actual cardiology. ...

Agents are not just chatbots with better manners Workflow automation used to be a polite arrangement. A human clicked a button, software followed instructions, logs were produced, and everyone pretended governance was mostly a documentation problem. Then AI agents arrived and made the arrangement less polite. An agent does not merely answer a question. It may search a database, call an API, write to a CRM, summarize private context, email a supplier, open a ticket, query a payment system, and decide which step comes next. That is the point. It is also the problem. ...

An AI system gives an answer. The answer looks plausible. The reasoning trace is long enough to seem serious. The user asks the next question, which is the one that actually matters: How sure is it? For ordinary software, this question is already annoying. For reasoning language models, it is worse. These models do not just emit a short response; they may spend thousands of tokens walking through a problem before landing on an answer. Asking them again is not free. Asking them eight times is not diligence. It is a budget line with philosophical decoration. ...

Users do not ask questions in benchmark format. They ask in fragments, emails, forms, meeting notes, support tickets, spreadsheet comments, and occasionally in the sort of sentence that makes a compliance officer stare silently at the ceiling. A business AI agent does not receive one clean canonical prompt. It receives the same task wearing many costumes. ...

A commercial building operator moves from monthly bill reviews and fragmented maintenance coordination to a governed AI-agent workflow that monitors energy patterns, protects tenant comfort, flags anomalies, and prepares owner-ready efficiency reports.

Battery. At 5 p.m., the grid wants help. The evening peak is approaching, the aggregator needs 3 kW of flexibility between 17:00 and 19:00, and one household in the portfolio looks promising. In the old demand-response world, this might become a price alert, an app notification, or a silent automated command. The household either complies, ignores it, or discovers later that the “smart” system has made a decision that feels less smart when dinner, laundry, or comfort is involved. ...

A chatbot can say yes to almost anything. That is part of the charm. It is also part of the problem. Ask an agent to “clean this dataset, train a model, compare alternatives, and generate a report,” and the conversation feels wonderfully frictionless. The system can interpret intent, improvise steps, write code, call tools, and explain itself in a tone that suggests adult supervision is somewhere nearby. ...