{"product_id":"the-context-window-is-a-ravi-vale-9798185413289","title":"The Context Window Is a Budget: Context Engineering for Reliable AI Agents and Long-Horizon Work","description":"\u003cp\u003e\u003cb\u003eBigger context windows didn't fix your agent. They hid the failure.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eYour agent worked perfectly in the demo. In production it forgets instructions, contradicts itself, and quietly gets worse the longer it runs. So you reach for a bigger model and a bigger window, and nothing improves, because the smooth sentences never sound any worse on the way down. You are not facing a model problem. You are facing a context problem, and almost no one has named it.\u003c\/p\u003e\u003cp\u003eEvery frontier model degrades as its input grows, measurably and predictably, even far below the token limit. The field calls it context rot, and it is the single biggest cause of agent failures in production. \u003ci\u003eThe Context Window Is a Budget\u003c\/i\u003e treats the fix as an engineering discipline: context engineering, the craft of deciding exactly what your AI agents see, when they see it, and what never enters the window at all. Optimizing what goes into the window beats enlarging it, every time.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside, the operating system for building reliable AI agents: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eContext rot, named\u003c\/b\u003e: why models get worse as input grows, and why you never hear it happen, since the prose stays smooth all the way down.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eThe attention budget\u003c\/b\u003e reframes the window the way a finance team treats money: a scarce allocation you spend deliberately, not a bucket you fill to the brim.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eWrite \/ Select \/ Compress \/ Isolate\u003c\/b\u003e: the four-move taxonomy turned into a working system you can ship this week.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eWhy bigger windows hide failures\u003c\/b\u003e instead of fixing them, and what actually goes wrong inside a long-horizon run.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eReliability that survives model upgrades\u003c\/b\u003e: curate what the agent sees, manage its memory outside the window, and stop rebuilding your stack on every release.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eCuration, not capacity, is the lever. Read it and you will stop blaming the model and start architecting what it sees, diagnose context rot before your users feel it, and hold your reliability through every upgrade, the one applied AI skill that does not expire in six months. Learn to spend the window.\u003c\/p\u003e\u003cp\u003eFor applied AI engineers, data scientists, and developers building retrieval augmented generation (RAG) pipelines, agentic RAG systems with rerankers, and long-running agents on the Model Context Protocol (MCP). Part of the Build Agents You Can Trust series, in The Verifier's Library.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Ravi Vale\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798185413289\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 07\/03\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 132\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.41lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.28d","brand":"Ravi Vale","offers":[{"title":"Paperback","offer_id":49174191112447,"sku":"9798185413289","price":24.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_77183528-32c0-4959-8ba5-af6057ff1e56.jpg?v=1788958256","url":"https:\/\/www.whiterainbookhouse.com\/products\/the-context-window-is-a-ravi-vale-9798185413289","provider":"WR Book House","version":"1.0","type":"link"}