AI Agents

Drug discovery is a wonderful place for AI demos. The model proposes a molecule, the molecule looks plausible, a docking score improves, and the slide deck starts to glow with that familiar color: almost-commercial blue. Then the evaluation protocol arrives and ruins the party. The problem is simple, and therefore easy to underestimate. A drug discovery agent is rarely asked to return one impressive molecule. It is asked to return a set of molecules that jointly satisfies several requirements: enough candidates, enough diversity, acceptable binding proxies, drug-likeness, synthetic accessibility, novelty, and other threshold-style constraints. One molecule can look good. A few molecules can look good. The final returned pool can still fail. ...

Agents are supposed to act. That is the promise hiding behind most enterprise AI demos: the model will not merely answer a question, but inspect a system, choose the next step, correct itself, and reach a useful outcome. The interface changes from chat box to workflow loop, and suddenly everyone starts using the word “agent” with the confidence of a person who has never watched a model get lost in a four-by-four grid. ...

A ticket lands in the queue. It looks ordinary: update a parser, answer a business question, patch a workflow, produce a SQL query. The agent opens the files, explores the schema, writes code, runs a few checks, and submits something plausible. The output is polished. The reasoning trace is confident. The dashboard marks the task as completed. ...

AI agents are excellent at finding the obvious answer. That sounds like a compliment until the task is to avoid everyone else’s obvious answer. Imagine three firms using AI assistants to screen applicants, forecast demand, or decide which customer segments deserve attention. If the goal is consistency, shared focal points are useful. Everyone reads the same policy, applies similar criteria, and avoids the usual mess of human improvisation. Lovely. The spreadsheet smiles. ...

Wall. That is not the grand philosophical frontier AI companies usually place in their product decks. The frontier is supposed to be reasoning, planning, tool use, autonomy, maybe a tasteful diagram with arrows and a glowing robot hand. But in a visually rich 3D world, a surprisingly large part of “autonomy” still reduces to something less glamorous: can the agent notice that it is stuck against a wall, step back, change angle, and continue? ...

Spreadsheet. That is where many impressive AI research reports quietly go to die. A model can browse twenty web pages, produce a polished executive memo, cite three market reports, and still fail at the boring part: comparing numbers, checking whether a table supports a claim, generating the right chart, and then explaining what the chart actually means. The output looks like research. The mechanism underneath is closer to literary confidence with a browser tab. ...

A number is a small thing. One integer in one answer. A seating capacity, a contract limit, a delivery quantity, a tax threshold, a credit exposure. Nothing dramatic. Certainly not the sort of thing that should become an architecture problem. Then an AI agent guesses it, sounds confident, stores the guess, and uses it again later. ...

Lunch is a simple word. In an AI assistant demo, “order me lunch” looks like the kind of request that should be easy by now. Open the food app. Pick something. Pay. Done. The button-clicking part is no longer the miracle. The problem is everything the user did not say. Do they avoid peanuts? Do they usually order from Tuantuan or Chilemei? Is “light lunch” about calories, price, time, or avoiding the food coma before a meeting? Should the assistant ask first, or does asking defeat the whole point of assistance? And if the user says no, does the assistant actually stop, or does it “helpfully” continue doing the wrong thing with the confidence of a junior consultant holding a fresh slide deck? ...

Memory sounds like a simple product feature. A user tells an assistant something today. The assistant remembers it tomorrow. Everyone applauds, the demo works, and someone writes “personalization” on a roadmap slide. Lovely. We have rediscovered a notebook. The harder problem begins when the user does not explicitly say what matters. A student says, “It’s fine.” A customer writes, “No worries.” A therapy-like support user replies with a short, polite sentence that looks neutral in isolation. Locally, the words are harmless. Historically, they may be resignation, guardedness, disappointment, or the emotional equivalent of quietly closing the door. ...

Collections. That is probably not the first word people expect in an article about emotionally intelligent AI agents. It sounds too ordinary, too administrative, too full of overdue invoices and politely threatening emails. Good. That is exactly why it is useful. Imagine an automated debt-recovery assistant calling a small business owner whose cash flow has collapsed. The assistant has a target: shorten repayment time. The debtor has a story: delayed receivables, layoffs avoided, a promise to pay later. A normal chatbot can respond with empathy. A larger model can produce warmer phrasing. A compliance-tuned model can avoid saying obviously illegal things, which is a charmingly low bar. ...