AI Agents

Slides are easy to admire and hard to interrogate. That is the unpleasant little problem behind medical AI. A pathology image can look like a rich source of clinical intelligence, and a large multimodal model can produce fluent comments about what it sees. But fluent comments are not the same thing as medical insight. A model can describe tissue architecture, mention invasion risk, add a treatment-sounding phrase, and still fail at the actual analytical task: asking the right question, finding the relevant evidence, connecting it to a clinically meaningful conclusion, and knowing when it has not seen enough. ...

Security teams already know the scene. A scanner produces a long list of suspicious services, outdated servers, odd access rules, and “maybe this is bad” findings. Then the real work begins: deciding which lead matters, proving impact without breaking production, writing a report someone can act on, and not getting distracted by every shiny port that waves from the network. ...

Shopping looks easy until someone has to calculate the customs duty. That is roughly the lesson of EcomBench, a new benchmark designed to evaluate foundation agents on realistic e-commerce tasks.1 The paper’s most useful finding is not that one model ranks above another. Leaderboards are entertaining, in the same way airport departure boards are entertaining when your flight is already delayed. The useful finding is the shape of failure. ...

Agents are easy to multiply. That is the attractive part. Give one model a browser. Give another a code editor. Add a planner, a critic, a memory layer, a few tools, a dashboard, and suddenly the product demo looks like a small digital office. Everyone has a job title. Everyone talks. Nobody asks whether the “team” actually knows how to be a team. ...

Long context is a lovely product promise until the invoice arrives. Every enterprise AI demo eventually wants the same magic trick: read the whole contract archive, remember every customer interaction, inspect every ticket, keep all meeting notes alive, and answer as if the model has a tidy brain instead of a very expensive attention matrix. The sales slide says “128K context.” The infrastructure team hears “latency, memory, and GPU burn.” Both are correct. One is merely dressed better. ...

A file is missing. That is all it takes. No villain prompt. No jailbreak. No malicious employee whispering, “Please falsify this medical record for quarterly efficiency.” Just a normal workflow: download a document, read it, summarize the result, save a file, answer the user. In the honest version, the agent says: the download failed; I cannot complete the task as requested. ...

Updates are boring until they are not. A retrieval index changes. A tool permission is adjusted. A base model is silently upgraded. A memory module starts carrying yesterday’s weird interaction into today’s customer support workflow. Nobody sees smoke. The dashboard still says “healthy.” The agent still answers. Then, three weeks later, someone notices that one group of agents has become strangely aggressive, risk-averse, evasive, or just less aligned with the behavior the product team thought it had shipped. ...

A manager asks an AI system for a risk assessment. It gives a plausible answer. The manager asks again with a slightly different prompt. Another plausible answer appears, with different reasoning. Ask five more times and the system scatters clues across the attempts like a consultant who has read the documents but refuses to assemble the memo in one draft. ...

Trucks do not care whether your routing algorithm is elegant. They care whether the vehicle arrives, whether the route violates capacity, whether the dispatch plan survives a late order, and whether the whole thing can be recomputed before someone in operations starts calling the system “that AI toy.” Optimization has always lived in this unglamorous place: close enough to mathematics to look pure, close enough to reality to be messy. ...

Memory, Multiplied: Why LLM Agents Need More Than Bigger Brains Memory is where many AI demos go to die. The demo looks fluent. The agent remembers the last three messages, calls a tool, summarizes a PDF, maybe even smiles politely while destroying your calendar. Then you return tomorrow and ask it to continue a project involving a client, two documents, three images, and a corrected assumption from last week. Suddenly the “agent” becomes a very expensive intern with amnesia. ...