Context Engineering

In short: AI agent cost optimization starts with context growth. A long-running agent can move from $50 a month to $2,500 because each turn resends system prompts, tool definitions, memory, files, and earlier messages. Four practices bring the bill back under control: prompt caching, lazy-loaded tools, model routing, and context cleanup. When an agent is new, the system prompt may be only a few hundred tokens, with two or three tools. Then the prompt grows, the tool list expands, memory accumulates, and every turn starts paying for earlier turns. The Claude system prompt leaked in late 2024 was 24,000 tokens, nearly 50 times larger than the starting point. OpenClaw users have reported sending more than 150,000 input tokens to Gemini 3.1 Pro, only to get 29 output tokens in the first turn. An unoptimized agent handling 100 messages a day with 166K input tokens can cost about $996 a month on Gemini 3.1 Pro and about $2,490 a month on Claude Opus 4.6. There are ways to push that cost back down to $50-$100 a month. ...

In short: Many AI agents look productive but are actually drifting — confidently executing the wrong moves on a wrong picture of the situation. The bottleneck for the next phase of agent systems is not larger context windows or stronger base models; it is whether the system can construct and maintain a stable belief state. This piece argues why belief state quality is the right optimization target, proposes five proxy metrics to measure it, and lays out where to put incremental engineering resources next. AI agents that look productive often turn out to be drifting — confidently executing the wrong moves on a wrong picture of the situation. Competition in agent systems is shifting from “whose model is stronger” toward “who can keep producing higher-quality belief state.” If you accept that framing, several seemingly unrelated problems suddenly line up: the same model behaves very differently inside different product shells; long-running agents fail not because they cannot answer but because their judgment of the situation is wrong; context windows keep growing, but system capability does not scale linearly with them; and scattered engineering pieces — skill, memory, retrieval, tool use, trace, summary — all start to matter at the same time. ...