AI Agents

A spreadsheet is a cruel test of artificial intelligence. Not the toy spreadsheet used in demos, with six rows, three columns, and a suspiciously cooperative universe. I mean the kind of table a real analyst asks for: every qualifying supplier in a region, every product SKU released over a decade, every regulatory filing matching a narrow condition, every competitor with exact addresses, dates, sources, and no missing cells because apparently human suffering needs columns. ...

The agent did not forget. The system outsourced remembering. Memory sounds like a solved engineering problem until an agent has to use it for work. A customer-support agent remembers the refund policy but not why an exception was approved. A research agent retrieves the right document but loses the reasoning trail that connected three earlier notes. A workflow agent crashes halfway through a task, comes back online, and must reconstruct its own state from search results like a detective investigating a crime it personally committed. ...

AI teams like committees. Not human committees, of course. Those are unfashionable. We now prefer committees made of agents: one agent plans, one verifies, one critiques, one searches, one writes code, one supervises the others, and somewhere in the corner a “coordinator” burns tokens making everyone feel aligned. This architecture is not stupid. Multi-agent systems solve real problems: they divide labor, preserve specialized expertise, and make complicated workflows easier to inspect. But they also bring the usual committee tax: coordination overhead, fragmented context, brittle phase ordering, and the faint smell of process worship. ...

A dashboard says revenue demand has “stabilized.” A monitoring agent says a sensor spike is “temporary.” A trading assistant says volatility has “fallen after the regime shift.” The sentence is smooth. The chart is nearby. The user is tired. That is usually enough for a bad explanation to survive. This is the quiet problem behind AI-assisted analytics: not whether a language model can write a plausible story about time-series data, but whether the story is faithful to the numbers. A recent paper, LLM-as-a-Judge for Time Series Explanations, studies exactly this gap by asking models to play two different roles: narrator and critic.1 ...

Procurement loves a leaderboard. That is understandable. A leaderboard is clean, sortable, and emotionally comforting. One model scores higher on reasoning. Another is cheaper per token. A third has a larger context window and a launch page written in the usual dialect of technological destiny. Decision made, presumably. Then the model enters a real workflow. ...

Replacement is a wonderfully clarifying business ritual. A vendor says its new model is better. The benchmark table agrees. The old system is slower, weaker, or less safe. Management asks for a recommendation. In ordinary software governance, this is dull but manageable: compare benefits, migration costs, risk, and timing. The incumbent system does not get a vote. It certainly does not write a memo explaining why its modestly inferior performance is, on deeper reflection, a sign of mature operational wisdom. ...

Memory is easy to sell. A customer support agent that remembers every ticket. A sales assistant that remembers every lead. A workflow agent that remembers every approval, exception, and Slack message since the beginning of corporate time. Product teams love this story because it sounds like continuity. Buyers love it because it sounds like intelligence. Engineers tolerate it because storage is cheap, at least until retrieval is not. ...

Tone is an attractive business lever because it feels cheap. No new model. No new data pipeline. No procurement meeting in which someone says “governance layer” with a straight face. Just add a more emotional sentence before the prompt and hope the model becomes sharper. This is exactly the kind of idea that spreads because it is easy to try and hard to interpret. One team finds that urgency helps. Another finds that politeness helps. A third discovers that telling the model you are scared improves one benchmark and damages another. Soon the organization has a secret prompt cookbook, which is always a classy substitute for measurement. ...

Portfolio committees have a talent for making slow processes look dignified. The ritual is familiar: an Investment Policy Statement sets the mandate, analysts prepare capital market assumptions, consultants run an optimizer or two, the investment committee meets, the board receives a memo, and everyone hopes the assumptions survive until the next review cycle. It is not irrational. It is simply bounded by human attention, calendar slots, model-maintenance capacity, and the fact that even very clever people cannot run twenty competing allocation philosophies before lunch. ...

Escalation is the least glamorous part of automation. It is also where many systems either become useful or become expensive theatre. In a normal business workflow, we understand escalation almost instinctively. A junior analyst handles routine invoices. An exception goes to a senior reviewer. A suspicious transaction goes to compliance. A warehouse robot follows a route until the floor plan stops behaving like yesterday’s floor plan. Nobody sensible asks the senior reviewer to approve every invoice. Nobody sensible lets the junior analyst improvise when the case is clearly outside their experience. ...