Prompt and Context Design for Enterprise Claude Agents

Ask two engineers why their Claude agent gives inconsistent answers and you'll usually get two wrong diagnoses: "the model isn't good enough" and "we need a bigger context window." Far more often the real problem is context design — what they chose to put in front of the model, what they left out, and how they ordered it. The context window is the agent's entire world for a given turn, and curating it well is the highest-leverage skill in agent engineering. This post is about doing it deliberately.

The counterintuitive truth is that more context frequently makes an agent worse. Every irrelevant document, stale instruction, and redundant tool result competes for the model's attention and dilutes the signal it needs. Good context design is as much about ruthless exclusion as inclusion.

The context window is a budget, not a bucket

Even with very large windows — the million-token class context available in Claude Code-style deployments — treat space as scarce. Context engineering is the practice of deliberately selecting, ordering, and compressing the information placed in a model's context window so it has exactly what it needs for the current step and no more. The discipline is to ask, for every chunk you're tempted to include, whether the model genuinely needs it to take the next action. If the answer is "maybe," it's probably noise.

The costs of overstuffing are concrete. Larger context means higher latency and higher token spend on every single turn, multiplied across thousands of conversations. And beyond cost, a bloated window measurably degrades decision quality — the model spends attention sifting relevance instead of reasoning. The teams whose agents feel sharp are the ones who keep the window lean, not the ones who dump everything in and hope.

What belongs in context

So what earns a place? A short, stable description of the agent's role and hard rules. The minimal task-relevant guidance for what it's doing right now. The current conversation, possibly summarized if it's long. The specific retrieved facts the next step needs — the three relevant policy snippets, not the manual. The results of recent tool calls. And the schemas of the tools the agent is currently permitted to use. That's roughly it, and the list is shorter than most teams' first draft.

flowchart TD
  A["New turn"] --> B["Pull candidate context: docs, memory, history, tools"]
  B --> C{"Relevant to THIS step?"}
  C -->|No| D["Exclude"]
  C -->|Yes| E{"Too long?"}
  E -->|Yes| F["Summarize / compress"]
  E -->|No| G["Include as-is"]
  F --> H["Order: stable first, dynamic last"]
  G --> H
  H --> I["Send to Claude"]

The diagram is the algorithm I run mentally for every turn: gather candidates, filter for relevance to this exact step, compress what's long, and order it stable-first so caching works and the model reads rules before volatile data. Everything that doesn't survive the relevance gate stays out of the window.

What to deliberately leave out

The harder discipline is exclusion. Leave out documents that are topically near but not needed for this step — near-misses are the worst noise because they look relevant. Leave out the full transcripts of completed sub-tasks once you've captured their conclusions. Leave out tool schemas for tools this agent can't use in this role. Leave out long preambles and motivational filler in the system prompt; the model doesn't need to be told the task is important.

Stale instructions are especially dangerous. If your prompt still contains a rule from an old version of the workflow, the model may follow it, and you'll chase a bug that's really an editing failure. Prune the prompt as aggressively as you'd prune dead code. A useful test: for every sentence in your system prompt, ask what would break if you deleted it. If nothing would, delete it. The prompt you keep is the prompt the model actually reads.

Ordering and the economics of caching

Order matters for two reasons. First, structure: putting the stable role and rules first and the volatile retrieved context last gives the model a clear frame before it sees the specifics — the same reason you brief a colleague on the goal before the details. Second, economics: prompt caching can reuse the unchanged leading portion of your context across requests, so a stable-first layout turns into real latency and cost savings on high-volume agents, while interleaving volatile content forfeits the cache entirely.

This is a case where the correctness-driven choice and the cost-driven choice happen to be the same choice, which is exactly the kind of design you want. Lay context out as immutable-then-mutable and you get a clearer prompt and a cheaper one. Make this layout a convention across all your agents so the savings and the clarity are automatic rather than per-agent heroics.

Managing context over long-running tasks

A single turn is easy; a long task is where context design earns its keep. As an agent works through a multi-step job, its raw history grows without bound, and if you carry all of it forward, every later turn gets slower, costlier, and foggier. The fix is progressive compression: when a sub-task finishes, summarize it down to the durable facts and conclusions, persist anything that needs to outlive the task, and drop the verbose history from the working window.

Done well, the agent carries forward a compact running state — "verified customer, confirmed order 4821 eligible, customer chose store credit" — instead of the full back-and-forth that produced it. This keeps late-task turns as crisp as early ones and is the practical reason some agents stay coherent across long, complex jobs while others degrade into confusion halfway through. Pair compression with a durable memory store so genuinely permanent facts survive even after the conversation ends.

Frequently asked questions

Does a bigger context window make an agent better?

Usually not. Beyond the raw cost and latency of more tokens, a bloated window dilutes the model's attention and degrades decision quality. Curating a lean, relevant context typically beats stuffing a large window full.

What should actually go in an agent's context?

Only what the next step needs: a short role-and-rules layer, minimal task guidance, the current (possibly summarized) conversation, the specific retrieved facts required, recent tool results, and the schemas of currently permitted tools. Exclude near-relevant noise and completed-task transcripts.

Why order context stable-first?

Two reasons: it gives the model the goal and rules before the volatile specifics, and it lets prompt caching reuse the unchanged leading portion across requests. The result is a clearer prompt and lower latency and cost on high-volume agents.

How do I manage context on long-running tasks?

Use progressive compression: when a sub-task finishes, summarize it to durable facts, persist anything that must outlive the task, and drop the verbose history. The agent carries forward a compact running state, keeping late turns as sharp as early ones.

Bringing agentic AI to your phone lines

CallSphere brings disciplined prompt and context design to voice and chat agents, keeping each conversation lean and focused so the agent answers accurately and books work in real time. 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.