Claude Prompt and Context Design for Agents

Ask most engineers why their Claude agent went off the rails and they will blame the model. Look closer and the cause is almost always the context — too much of the wrong thing, too little of the right thing, in an order that buried what mattered. Context design is the most underrated skill in agent building. A capable model with a polluted context performs worse than a smaller model with a clean one. This post is about deciding, deliberately, what goes into an agent's context and what stays out.

The framing that helps most is to treat context as a scarce, attention-limited resource rather than free storage. Even with a million-token window, everything you add competes for the model's attention and dilutes the signal. The job is curation, not accumulation. Below are the principles I use to keep agent context lean, ordered, and effective inside an orchestration system.

Context is a budget, not a bucket

Start from the premise that context engineering is the practice of deciding which information enters a model's working context for a given step, and in what order, so the model attends to what matters. Every token you include should earn its place by changing what the agent should do. The agent's objective, the specific inputs it needs, the rules it must follow, and the tools it can call — those earn their place. The entire upstream conversation, unrelated history, and speculative "just in case" data usually do not.

The failure mode of ignoring this is subtle. The agent does not crash; it gets vaguer. It hedges, it half-follows instructions, it loses track of the one constraint that mattered because it was on line ninety of a hundred. Tightening context fixes problems that look like model weakness but are really signal-to-noise problems. When an agent underperforms, my first move is to remove, not add.

What to put in, what to leave out

Concretely, a well-designed agent context contains: a stable role and behavior statement; the current objective; the minimal set of facts and prior results this step depends on; the output contract; and explicit constraints. It leaves out other agents' raw working notes, full documents when a relevant excerpt would do, resolved tangents, and anything the agent can fetch on demand through a tool rather than carry by default.

flowchart TD
  A["Incoming step"] --> B{"Is this needed to act?"}
  B -->|No| C["Leave out / fetch via tool later"]
  B -->|Yes| D{"Stable or per-step?"}
  D -->|Stable| E["Cached prefix: role & rules"]
  D -->|Per-step| F["Objective, inputs, contract"]
  E --> G["Assembled context"]
  F --> G
  G --> H["Claude reasons & acts"]

The decision tree in the diagram is the discipline in miniature. For every candidate piece of information, ask whether the agent needs it to act this step. If not, leave it out — it can be retrieved through a tool later if it turns out to matter. If yes, sort it into the stable prefix or the per-step body. That sorting is not just tidiness; it directly enables prompt caching, which we will get to.

Order and structure shape attention

Where you place information changes how well the model uses it. Put the stable, unchanging material first — the role, the persistent rules — and the variable, task-specific material last, closest to where the model starts generating. This ordering keeps the most immediately relevant content fresh and, just as importantly, lets the unchanging prefix be cached so you are not paying to reprocess it on every call.

Within the body, use clear structural markers so the model can navigate: a section for the objective, one for inputs, one for the contract, one for constraints. Claude reads structured prompts more reliably than run-on text, and the structure also helps you, the engineer, audit what an agent actually saw. When you later debug a bad run, a well-sectioned context is a readable record; a wall of concatenated strings is a mystery.

Managing context over a long run

Orchestrations that run for many turns face a different problem: context grows as the agent accumulates tool results and reasoning. Left unmanaged it bloats until it crowds out the objective or blows the budget. The fix is active context management. Periodically compact the history into a compact summary of decisions and durable facts, and drop the raw intermediate exchanges. Keep the durable conclusions; discard the scaffolding that produced them.

A second technique is to externalize memory. Instead of carrying everything in context, write durable facts to a store — a scratchpad, a state record, a file — and let the agent retrieve them when needed. This is exactly where the shared state store from your orchestration layer earns its keep: agents read the specific prior result they need rather than dragging the whole history along. The context stays small and sharp even as the run grows long, which is the only way long Claude agent runs stay reliable.

Retrieval over preloading

The instinct to stuff every potentially relevant document into context is usually wrong. Prefer retrieval: give the agent a search or fetch tool and let it pull the specific passage it needs at the moment it needs it. This keeps the default context lean and, counterintuitively, often improves accuracy, because the agent reads a focused excerpt instead of skimming a giant blob. Preload only what is needed on nearly every step; let everything else be fetched on demand. The result is a system that scales to large knowledge bases without ballooning per-step cost or drowning the model in irrelevant text.

Frequently asked questions

If Claude has a huge context window, why not include everything?

Because attention is finite even when the window is large. Extra tokens dilute the signal, slow the call, and cost more, and the agent attends less reliably to the constraint that actually mattered. A lean, well-ordered context consistently outperforms a maximal one on real tasks.

How do I keep context small in a long-running agent?

Compact periodically — summarize decisions and durable facts, discard raw intermediate exchanges — and externalize memory to a state store the agent retrieves from on demand. The goal is that context size tracks what the current step needs, not the full length of the run so far.

What belongs in the stable prefix versus the per-step body?

The prefix holds unchanging material: the role, persistent behavior rules, and shared instructions. The body holds per-step content: the objective, the specific inputs, the output contract, and constraints. Putting stable content first makes it cache-friendly and keeps variable content close to where generation begins.

When should I retrieve instead of preload?

Preload only what nearly every step needs; retrieve everything else through a tool. If a document is relevant occasionally, give the agent a way to fetch the right excerpt on demand rather than carrying the whole thing by default. This keeps context lean and often improves accuracy.

Bringing agentic AI to your phone lines

CallSphere applies this same context discipline to voice and chat agents — feeding each turn exactly the caller facts and tools it needs and nothing more — so its assistants stay sharp across long conversations and book work reliably. Hear it for yourself at callsphere.ai.

Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.