Not vibe-coding.
Structured systems.
Deliberate tooling.

There's no persistent memory. Every time you send a message, the entire conversation is re-read from the top. The model isn't remembering — it's reading a growing transcript and predicting the next logical response.

This is why context management matters. As the conversation grows, the model burns more tokens just catching up on history — leaving less headroom for actual work. You want to operate inside the context window, not push against the edge of it.

Not every prompt needs the most powerful model. A hardness-based router scores each request on complexity and routes it to the cheapest model capable of answering well.

Simple questions hit a fast, cheap model or local inference. Architecture problems hit something heavyweight. The workflow doesn't change — the routing happens underneath automatically.

Standard web search makes an AI fetch entire pages, burn thousands of tokens reading them, then extract one useful sentence. A search API like Tavily does the extraction first — it hands the AI a compressed, relevant result instead of a raw HTML dump.

Same answer. A fraction of the cost. This is the difference between a tool that does one thing perfectly and one that does everything expensively.