AI Agents

Stop Every production LLM workflow eventually meets the same boring question: should the model answer now, think again, or throw away the current path and try something else? That question sounds less glamorous than “build a bigger model.” It is also closer to where real deployment costs live. Reasoning models can improve by sampling more answers, extending chains of thought, or running repeated critique-and-revision loops. The bill, naturally, arrives in tokens, latency, GPU capacity, and engineering patience. The last item is rarely benchmarked, perhaps because it would make too many papers look expensive. ...

Workflow automation has a bad habit of looking impressive right up to the moment it touches reality. A demo agent can summarize a refund policy, draft a polite message, and call a refund_order() tool with great confidence. Then the real workflow asks a boring question: does this order exist, is it within the refund window, has it already been refunded, does the customer’s loyalty tier matter, and should the database state change after approval? ...

A benchmark is supposed to be a ruler. In AI, it often becomes a trophy shelf. A model gets a higher score, a chart moves up and to the right, and everyone politely pretends the hard part has been settled. That ritual works when the task is narrow: classify an image, answer a question, pass a coding test, retrieve a document. But it becomes much less comforting when the system being evaluated is no longer just answering. It is planning experiments, writing code, debugging failures, training models, interpreting results, and deciding what to try next. ...

Code review has a comforting ritual. A developer submits a patch. A reviewer inspects it. The reviewer says it looks good. Everyone feels slightly better, because at least someone checked. In AI-agent workflows, this ritual becomes even more tempting: let one agent write the patch, let another agent review it, then ask the reviewer how confident it is. ...

Memory is usually where agentic systems go to become expensive. That is not the glamorous failure mode. It is not the cinematic robot rebellion, nor the slightly more realistic spreadsheet full of hallucinated invoices. It is quieter: an LLM agent keeps improving a program, stores previous attempts, retrieves a few “good” ones, and then spends half its context window rereading code scaffolding that no longer explains anything useful. ...

AI agents do not need feelings to become difficult to read. That is already enough trouble. A long-running agent can enter a workflow, absorb context, make decisions, and gradually behave as though the situation has a particular “shape.” The system may not merely react to the latest input. It may carry forward a learned orientation: this client is risky, this process is stable, this market regime is noisy, this user wants speed more than precision. In ordinary product language, we call that “context.” In engineering dashboards, we often reduce it to memory, state, embeddings, or hidden activations. In philosophical language, one might be tempted to call it a perspective. ...

Agent teams are easy to draw and hard to run. On a slide, the architecture looks comforting: a planner, a researcher, a coder, a reviewer, perhaps a compliance agent standing in the corner with a clipboard. Everyone has a role. Everyone collaborates. The diagram is tidy, which is usually the first warning sign. ...

Search is supposed to make language models safer. The model does not know something, so it searches. It finds evidence, reasons over that evidence, and gives a better answer. Very civilized. Very responsible. Then the first search query goes slightly wrong. The model retrieves a relevant-looking but misleading paragraph. It builds the next reasoning step around the wrong entity. The next query becomes narrower, but in the wrong direction. The final answer may still sound fluent, because fluency is the one department where language models rarely file sick leave. The actual reasoning chain, however, has already drifted. ...

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. ...

A user says, “Update the record with a sensible value.” That sentence is small. The damage may not be. For a normal chatbot, the worst outcome might be a vague answer wearing a confident expression. Annoying, yes, but usually recoverable. For an agent connected to a database, file system, workflow platform, or API service, the same ambiguity becomes operational. The model may update the wrong row, call the wrong endpoint, overwrite a file, or politely explain its mistake after making it. Charming, in the same way a self-driving forklift is charming. ...