Most advice about context lives at the prompt level: phrase the instruction well, give a couple of examples, add the relevant snippet. That advice is fine, and it is also the wrong altitude once you are building a system rather than a single call. At system scale, the context window stops being a prompt detail and becomes a hard architectural constraint, a finite budget that every component of your agent quietly competes for.

Teams that treat context window architecture as a first-class design concern build agents that stay coherent as they grow. Teams that treat it as something the prompt layer will sort out watch their agents get slower, more expensive, and less accurate for reasons that are hard to trace, because the cause is spread across the whole system rather than sitting in one bad prompt.

The window is a budget, not a bucket

The instinct, once you have a large context window to work with, is to treat it as a bucket: pour in the instructions, the relevant files, the conversation history, the tool outputs, and let the model sort it out. Bigger windows encourage this, because you can.

That instinct is wrong for two reasons that both get worse with scale. The first is cost: every token you place in the window is paid for on every call that carries it, and a system that reflexively includes everything pays for a lot of tokens that changed nothing and helped nothing. The second is quality, which is the less obvious one. A window packed with marginally relevant material does not help the model find the signal. It buries it. Past a point, adding context makes the output worse, not better, because the thing that mattered is now one paragraph among fifty.

So the right mental model is a budget. You have a finite allocation, everything wants a share, and your job as the architect is to decide who gets what. A budget forces the useful question that a bucket lets you avoid: for this specific step, what is the minimum set of things the model actually needs?

Budget per step, not per system

The most common mistake is to think about context at the level of the whole system, assembling one big context blob and reusing it everywhere. It feels efficient and it does not scale, because different steps of an agent need genuinely different things.

A planning step needs the goal, the constraints, and a map of the territory. An implementation step needs the specific interfaces and call sites it will touch. A verification step needs the acceptance criteria and the diff. Handing all three the same maximal context wastes budget on every one of them and dilutes the material each actually depends on. The discipline is to budget per step: decide, for each unit of work, the slice that step needs, and give it that and little else. This is the same principle that makes handoffs and contracts between agents work, viewed through the lens of what each side has to carry.

What to keep, what to summarize, what to drop

Budgeting per step means sorting candidate context into three tiers, and being honest about which is which.

Keep, verbatim. The task, the constraints that must hold, and the specific code or data the step operates on. This is the load-bearing material, and it earns its full-fidelity place.

Summarize. Long histories, prior decisions, and background that matters as shape but not as detail. A running summary that captures the decisions without replaying every token is far cheaper than the raw transcript and usually just as useful. This is where the biggest savings hide, because history is what grows without bound.

Drop. Anything stale, superseded, or off-path for this step. Old tool outputs, resolved sub-problems, files the step will not touch. Dropping is uncomfortable because it feels like losing information, but keeping everything is how the signal gets buried.

The hard part is that these tiers are a judgment, not a folder. "Relevant" is decided per task, and getting it right is most of the work. But even a rough version of this discipline beats the bucket, because the bucket makes no decision at all.

Position matters, not just volume

There is a second-order effect worth designing around: models do not attend evenly across a long context. Material in the middle of a very large context tends to get less attention than material at the beginning or the end. So it is not only how much you include, it is where you put it.

The practical consequence is that the most load-bearing instructions and the most relevant code should sit at the edges of the context, not buried in the middle of a long dump. If your architecture assembles context by concatenating everything in arrival order, you are leaving this on the table. Placing the critical material deliberately is a cheap win that a bucket approach cannot make.

Context is a cross-cutting concern

The reason this belongs in an architecture discussion rather than a prompting one is that context is spent by everything. Every tool that returns output, every retrieval step, every summary, every piece of history is drawing on the same shared budget. If no part of the system owns that budget, it gets overspent by accretion: each component adds "just a little more," and the total quietly blows past the point of diminishing returns.

So give the budget an owner. Decide, at the system level, how context is assembled and trimmed, rather than letting each component push whatever it likes into the window. That decision interacts with the rest of your architecture: it shapes how you stream and parse output when responses get large, and it constrains how portable you are across models, because different providers offer different window sizes and your budget has to survive a swap to a smaller one.

The honest limitation

Context budgeting raises the ceiling on how large and coherent an agent system can get. It does not make a vague task specific or a wrong plan right, and it cannot recover information you chose to drop but turned out to need. Summarization is lossy by definition, and every summary is a bet about what mattered. Window sizes keep growing, which tempts teams to think the constraint is going away, but a bigger budget spent carelessly is just a bigger bill and a bigger haystack. The discipline outlives the limit.

There is also no universally correct allocation. The right budget depends on the task, the model, and the cost you are willing to bear, and it changes as any of those change. Treat it as something to tune against real behavior, not a setting you get right once.

This is a constraint we design around explicitly at Loopsfinity: each unit of work gets the slice of context that step needs rather than everything the system knows, because the goal is the right context, not the most. The details of how we decide that are our own work. The principle is not, and it is worth holding any agent system to: budget the window deliberately, or watch it get spent for you. It is one of several forces covered in AI agent architecture.