Prompt and Context Design for Claude Agents That Work

Two agents with the same model and the same tools can perform wildly differently, and the variable is almost always context. The model is fixed; what you feed it is the lever you control. Yet most teams treat context as an afterthought — dump in everything plausibly relevant and hope. This post argues the opposite discipline: context is a designed artifact, and what you leave out matters as much as what you include. Get this right and a mid-tier model outperforms a stronger one drowning in noise.

Context is a signal-to-noise problem

The temptation with a large context window is to use all of it. Resist. Every token you add that doesn't bear on the current decision is noise that competes with the tokens that do. An agent given the failing test, the one function under repair, and the relevant convention will fix the bug. The same agent given those things plus the entire repository will sometimes wander into an unrelated file because something there caught its attention. More context is not more help; it's a worse ratio.

The working definition to hold onto: context design is the practice of choosing the minimal set of information that lets the agent make the current decision correctly, and deliberately excluding everything else. "Minimal" and "current decision" are the load-bearing words. You're not assembling a reference library; you're staging exactly what this step needs.

The four things worth their tokens

In practice, four categories of content reliably earn their place. Stable instructions — the agent's role, hard constraints, and house conventions — usually live in a file like CLAUDE.md and stay constant across tasks. Task specifics — the exact objective and acceptance criteria. Just-the-relevant code and data — the specific files, the failing output, the one schema in play. And tool definitions — descriptions of what the agent can call. Anything that doesn't fall into these is a candidate for cutting or summarizing.

The diagram below shows how an incoming task gets assembled into a focused context rather than a dumped one — each potential input is triaged before it's allowed in.

flowchart TD
  A["Incoming task"] --> B["Load stable instructions"]
  A --> C["Retrieve candidate files"]
  C --> D{"Bears on this task?"}
  D -->|No| E["Drop or link only"]
  D -->|Yes| F["Include focused excerpt"]
  B --> G["Assemble context window"]
  F --> G
  G --> H["Agent acts with dense context"]
  E --> H

Notice that retrieval feeds a filter, not the context directly. Pulling fifty candidate files is fine; pasting all fifty is not. The filter step — keep the few that bear on the task, link or drop the rest — is where context design actually happens.

What to deliberately leave out

Some things feel helpful but hurt. Leave out stale documentation that contradicts the current code; the model can't tell which to trust and may follow the wrong one. Leave out unrelated files pulled in by a broad search. Leave out long chat histories once they've served their purpose — old turns about a solved subproblem just crowd the window. And leave out redundant restatements; saying the same constraint three ways doesn't triple its weight, it just spends tokens.

A subtler one: leave out information the agent can fetch on demand. If a fact lives behind a tool the agent can call, you often don't need it pre-loaded. Pre-loading everything "just in case" is the habit that bloats context. Trust the tool layer and the retrieval step to supply specifics when a particular step needs them, keeping the baseline context lean.

Use skills and just-in-time loading

The cleanest way to keep context lean while still having depth available is dynamic loading. Agent Skills are folders of instructions, scripts, and resources that Claude loads only when a task matches their description, which means a hundred specialized procedures can exist without any of them sitting in context until the moment they're relevant. This is context design as architecture: instead of one fat prompt that tries to cover every situation, you have a small core plus a library of capabilities that swap in on demand.

Apply the same just-in-time mindset to data. Rather than loading a service's entire config at the start, let the agent retrieve the specific section when it reaches a step that needs it. The result is that at any given moment the context reflects the current step, not the union of everything the task might ever touch. That focus is what keeps long-running agents sharp instead of letting them drift as the window fills.

Order and structure within the window

Where you put things inside the context matters, not just what you include. Lead with the stable role and constraints so they frame everything that follows. Put the specific task and the most decision-relevant material where it's easy to anchor on. Use clear structure — labeled sections, fenced code, explicit headings — so the model can tell the instruction from the data from the example. A wall of undifferentiated text forces the model to do parsing work that good structure would have done for free.

Be especially careful to separate instructions from untrusted input. When the agent reads data it fetched from the outside world — a web page, a user message, a ticket body — mark it clearly as content to reason about, not commands to obey. Blurring that line is how prompt-injection attacks slip through. Structure isn't only about clarity; it's also a security boundary.

Measure and tighten over time

Context design is empirical. When an agent makes a wrong move, the post-mortem question is usually "what did it have in context, and what did it lack?" Often the fix isn't a cleverer prompt but a context change — add the one missing fact, remove the misleading doc, tighten a vague instruction. Keep your stable instructions and skill descriptions in version control and treat edits to them as real changes, reviewed and tested. Over many iterations this is how the baseline context gets sharp, and sharp context is the quiet reason your agents become reliable.

Frequently asked questions

If the context window is huge, why not just fill it?

Because relevance, not capacity, drives quality. Irrelevant tokens dilute the signal and can pull the agent toward the wrong file or fact. A focused context usually beats a full one, even when the full one fits.

How do I decide what to leave out?

Ask whether each item bears on the current decision. If it doesn't, drop it or replace it with a link the agent can follow on demand. Stale or contradictory material is an especially high priority to remove.

Where do Skills fit into context design?

Skills let depth live outside the baseline context and load only when relevant, so you get specialized procedures without paying their token cost on every task. They're the main tool for keeping context lean while staying capable.

Does context structure affect security?

Yes. Clearly separating trusted instructions from untrusted fetched data is a defense against prompt injection. Label external content as material to reason about, never as commands to follow, and the agent is far less likely to be hijacked.

Bringing agentic AI to your phone lines

CallSphere brings disciplined context design to voice and chat — agents that carry just the right context into every call, load skills on demand, and act on tools mid-conversation to book work 24/7. Listen to the difference at callsphere.ai.

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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.