Waiting is the least glamorous part of an AI agent. A user asks for a report, a code fix, a dataset analysis, or a literature scan. The agent thinks, calls a tool, waits, reads the result, thinks again, calls another tool, waits again, and repeats this little ritual until the final answer appears. From the outside, this looks like “reasoning.” From the system side, much of it is simply queueing around tools. ...
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. ...
Memory sounds simple until it becomes a product requirement. A sales assistant must remember that one client refuses cloud deployment. A software agent must remember that Redis was vetoed after a production incident. A research copilot must remember which hypothesis failed three weeks ago, not because it is charmingly nostalgic, but because repeating failed work is an expensive hobby. ...
A workflow breaks in a boring way. The agent found the website yesterday. Today the button moved. Yesterday it parsed the file path correctly. Today the file name has a space, a date, and some human creativity sprinkled in for punishment. Yesterday the chart script worked. Today the data source changed its column names because apparently stability was not on the roadmap. ...
Logs are where agentic AI gets honest A business agent rarely fails in the dramatic way demo videos imply. It does not usually announce, with theatrical humility, that it has misunderstood the workflow, misread the screen, or built a wrong model of the task. More often, it produces a tidy chain of steps, a reasonable explanation, a few reassuring intermediate notes, and then quietly stores the wrong conclusion as if it were company policy. ...
CRM is supposed to prevent organizational amnesia. The sales team learns that a prospect is evaluating three vendors. Support later discovers that the same company is unhappy with integration quality. Marketing has a note that the buyer prefers technical benchmarks over executive storytelling. Finance knows the renewal is sensitive to payment terms. ...
Discount. It is a small word, but in retail it is not decorative. It changes what people buy, how much they buy, whether they switch brands, whether they stockpile, whether distributors clear inventory, and whether a manager later pretends the promotion was “strategic” rather than simply expensive. This is where many LLM-agent demos become fragile. They can describe a discount. They can explain why a rational consumer might respond to it. They can even role-play a price-sensitive shopper with theatrical enthusiasm. But describing incentive response is not the same as simulating it. A consumer simulator that treats price as one more piece of text is not an economic simulator. It is a chatbot wearing a shopping cart. ...
Memory is usually sold as the adult upgrade for LLM agents. Give the agent a past. Give it a vector database. Give it episodes, reflections, mistakes, summaries, and a long enough context window to remember every tiny embarrassment. Surely it will become more reliable. The RPMS paper is useful because it interrupts that comforting story with a less fashionable point: memory can make an agent worse when the world has hard action rules.1 ...
A failed automation run usually tells you more than a successful one. A coding agent compiles the wrong program and receives a concrete error. A web-navigation agent clicks into the wrong product page and sees that the attributes do not match. A task agent tries an invalid action and the environment complains, patiently, like a machine that has seen too much. In each case, the system does not merely say “failed.” It gives clues. ...
Slides are supposed to be obvious. That is their entire professional excuse for existing. A good presentation does not merely contain information; it makes the intended argument recoverable by someone who was not inside the author’s head. This is why a deck can look expensive and still fail. The gradients are polished, the icons are friendly, and the narrative has quietly wandered into a swamp wearing a consultant’s blazer. ...