Computing Life · Share · YageJul 8, 2026, 08:00 UTC+8

Why agents need context governance beyond bigger windows

模型的窗口不是无限大的垃圾桶：为什么智能体需要上下文治理

More tools mean more noise in the context window. Anthropic's MCP sandbox cuts 150K tokens of tool definitions down to ~2K of high-signal input. Google ADK splits agent state into working context, session state, long-term memory, and file artifacts—intermediate outputs stay off-prompt by default. Manus reports a ~100:1 input-to-output token ratio in production; they keep raw files in a sandbox, stabilize tool-call formats for KV cache hits, and rewrite a todo.md at the window's end to fight lost-in-the-middle. Headroom compresses JSON and logs by 60–95%, but lacks large-scale validation on hard coding tasks. The takeaway: RAG is the foundation, but the real engineering is runtime information governance.

Why it matters: Hits all three HKR axes with concrete engineering numbers and cross-framework comparison. Docked because it's a personal blog, not an official release, and the excerpt cuts off mid-argument — low featured band at 78.

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Take

I clicked on this because it names a problem every agent builder eventually hits: the more tools you plug in, the more the model's attention gets buried in noise. A user asks "what went wrong with yesterday's deploy," and the system stuffs 20K tokens of tool definitions, logs, and scraped HTML into the context window first. The actual question drowns.

The piece does a clean job connecting approaches from several teams. Anthropic's MCP sandbox compresses 150K tokens of tool definitions down to roughly 2K of high-signal input by separating the control plane from the data plane. Google ADK splits agent state into working context, session state, long-term memory, and file artifacts—intermediate outputs stay off-prompt by default. Manus is the most production-grounded: they report a ~100:1 input-to-output token ratio, stabilize tool-call formats to maximize KV cache hits, and rewrite a todo.md at the window's end to fight lost-in-the-middle.

I'd discount the Headroom section a bit. They claim 60–95% token reduction on JSON and logs, but the article itself admits there's no large-scale validation on hard coding tasks. If your source material is already compact, adding a compression proxy might break cache alignment and add latency instead.

The bigger point isn't that RAG is useless—it's that RAG only handles the "where to fetch" half. What happens after retrieval—scheduling, compression, discarding stale state—is a separate runtime governance problem. Using the filesystem as storage and letting the model pull pointers on demand is a more practical instinct than blindly throwing bigger windows at the problem.

Source: Computing Life · Share · Yage · yage.ai
Published: Jul 8, 2026, 08:00 UTC+8 · Jul 8, 2026