Test-Time Learning

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. ...

A team meeting usually ends with someone saying, “Let’s remember this for next time.” Human teams sometimes do. Agent teams usually do not. A group of LLM agents can debate, critique, revise, and produce a final answer. Then the whole episode often disappears into the landfill of inference logs: useful comments, bad guesses, decisive objections, elegant checks, all flattened into “the model answered correctly” or “the model failed.” Very modern. Very wasteful. ...

Tickets repeat. Spreadsheets repeat. Compliance reviews repeat. Code reviews repeat. Not exactly, of course. That would be merciful. They repeat with just enough variation to make last month’s solution almost useful and therefore mildly dangerous. This is where many enterprise “AI memory” systems become filing cabinets with delusions of competence. They store prior chats, snippets, tickets, documents, and summaries, then hope the next prompt will rhyme closely enough with something in the archive. Sometimes it does. Often it does not. The agent remembers the old puzzle, not the transferable piece. ...