Topic, page 4
Page 4 of articles on AI.
Reason, act, observe, repeat is the shape almost every agent uses. Its weaknesses are structural, and they show up in the same three places every time.
Cost, latency and the point at which text gets cut off are all counted in tokens. Reasoning about the system in words will mislead you in all three.
When an agent misuses a tool, the instinct is to rewrite the prompt. Usually it's the tool's own signature that made the mistake available in the first place.
Request logs tell you a call succeeded. For a model-driven system the useful record is the whole chain, because the mistake is rarely in the last step.
A model that describes an image accurately is not yet a system. Frame rate, cost per frame and what happens on a wrong answer decide the architecture.
A million-token window suggests you can stop being selective about context. Attention is uneven across a long window, and cost scales with what you send.
Embeddings capture how text is used, not what it means. That distinction explains most of the retrieval failures that look inexplicable from the outside.
Most things called agents are workflows with a model bolted on. The line is whether the system picks its own next step, and everything difficult follows.
MCP gets described as USB-C for AI tools. The useful question is narrower: which parts of tool integration stop being your problem, and which never were.
Running a model locally is free in the sense that a server is free. The bill is idle capacity, and the arithmetic decides this more than principle does.
An MCP server turns an internal API into something a model calls on its own initiative. The threat model changes, and the existing auth boundary rarely fits.
Most problems described as agent problems are retrieval, a workflow, or a form. Agents earn their cost in a narrow band, and it pays to know where it starts.
Whether it needs designing from scratch or rescuing from its own success, tell me what you’re building and I’ll tell you how I’d architect it.
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