Agentic AI

A lab is full of reviews. A candidate molecule is screened, criticized, scored, filtered, re-ranked, re-tested, and then quietly abandoned because one property looked promising while three others looked inconvenient. Drug discovery has never lacked opinions. It has lacked a clean way to convert those opinions into a machine-readable optimization process. That is the useful point in MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design.1 The paper is easy to misread as another “LLM designs molecules” story. That would be tidy, familiar, and slightly wrong. ...

A mid-sized event agency moved from human-coordination-heavy planning across chats and spreadsheets to a human-reviewed agentic workflow that centralizes event state, flags exceptions, and keeps vendors, budgets, guests, schedules, and risks aligned.

Budget meetings have a useful cruelty. They make vague AI strategy sound ridiculous. A team may begin with the familiar story: the model is not reasoning well enough, so the company needs a larger model, a longer context window, more inference-time search, and probably a procurement conversation involving GPUs. Very modern. Very expensive. Also not always the right diagnosis. ...

A customer-service agent rebooks a flight, checks a policy, calls an API, updates the passenger record, apologizes politely, and still gets the outcome wrong. The old explainability question would be: which input tokens influenced the final answer? That question is not useless. It is just late to the crime scene. When an AI system only predicts, explanation can focus on a single input-output decision. When an AI system acts, explanation has to follow the behavior across time: the state it maintained, the tool it selected, the observations it received, the recovery move it attempted, and the point where the run quietly became unrecoverable. A nice feature-importance chart does not tell you that. It tells you what mattered to a prediction, not how a workflow failed. ...

Security teams do not need an AI that sounds decisive. They already have enough decisive systems. Some of them are called “legacy tools.” Some are called “urgent executive dashboards.” A few are called “we should probably reboot it.” What security operations need is more uncomfortable: an AI system that can propose useful response actions, explain why they might work, and then refuse to act when its own reasoning becomes unstable. That refusal matters. In an incident-response workflow, a hallucinated recommendation is not merely a bad paragraph. It can isolate the wrong host, patch a vulnerability that does not exist, wipe evidence too early, or generate a playbook that looks official while quietly wasting the first thirty minutes of response time. ...

Procurement looks boring until the software starts spending money. A human buyer can be slow, inconsistent, and occasionally allergic to spreadsheets. But at least we know what failure looks like: overpaying, accepting bad terms, walking away too late, or trusting the wrong supplier. When the buyer is an LLM agent, the failure mode becomes more polished. It can overpay in fluent English. It can miss a deal while sounding reasonable. It can keep bargaining after the answer is already visible. Progress, apparently, now comes with better punctuation. ...

A diagram is often where a paper stops being private reasoning and becomes public knowledge. Before that point, the author may have a method, a theorem, a pipeline, or a system architecture. The reader has only paragraphs. Then one good figure appears, and the fog lifts. The method has stages. The variables have roles. The arrows tell us what depends on what. The paper becomes less of a swamp. ...

A support agent keeps asking the same diagnostic question after the customer has already answered it. A research agent revisits the same failed source path with slightly different wording. A workflow agent tries the same invalid action again because, apparently, the best evidence for what to do next is what it just did badly. ...

Agentic AI has a habit of sounding more engineered than it is. A demo connects an LLM to a search tool, adds a memory store, wraps the whole thing in a planner, and suddenly the slide deck says “autonomous agent.” The system may still forget what it just saw, retrieve the wrong context, misuse tools, loop on bad actions, or politely hallucinate its way into a support ticket. But the diagram has arrows, so morale remains high. ...

Latency is where elegant AI architectures go to become invoices. A neuro-symbolic system looks clean on a slide: a neural model sees patterns, a symbolic module checks rules, a probabilistic module handles uncertainty, and the final system behaves more reliably than a pure neural model improvising under fluorescent lighting. Lovely. Very architectural. Very responsible. ...