---
title: "Claude API Prompt Pattern: Agent Design"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/spec-claude-api-prompt-pattern-agent-design"
modality: "IDE_RULE"
category: "Framework IDE Rules"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Claude API Prompt Pattern: Agent Design

**Summary:** Claude API Prompt Pattern: Agent Design is a verified IDE RULE blueprint on Promptally in Framework IDE Rules optimized for leading AI models. Cursorrules Mdc specification for Claude API Prompt Pattern: Agent Design. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | IDE_RULE |
| Category | Framework IDE Rules |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 2,183 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

```mdc
# Agent Design Patterns

This file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage context and cost over long runs. For per-tool mechanics and code examples, see `tool-use-concepts.md` and the language-specific folders.

---

## Model Parameters

| Parameter | When to use it | What to expect |
| --- | --- | --- |
| **Adaptive thinking** (`thinking: {type: "adaptive"}`) | When you want Claude to control when and how much to think. | Claude determines thinking depth per request and automatically interleaves thinking between tool calls. No token budget to tune. |
| **Effort** (`output_config: {effort: ...}`) | When adjusting the tradeoff between thoroughness and token efficiency. | Lower effort -> fewer and more-consolidated tool calls, less preamble, terser confirmations. `medium` is often a favorable balance. Use `max` when correctness matters more than cost. |

See `SKILL.md` §Thinking & Effort for model support and parameter details.

---

## Designing Your Tool Surface

### Bash vs. dedicated tools

Claude doesn't know your application's security boundary, approval policy, or UX surface. Claude emits tool calls; your harness handles them. The shape of those tool calls determines what the harness can do.

A **bash tool** gives Claude broad programmatic leverage - it can perform almost any action. But it gives the harness only an opaque command string, the same shape for every action. Promoting an action to a **dedicated tool** gives the harness an action-specific hook with typed arguments it can intercept, gate, render, or audit.

**When to promote an action to a dedicated tool:**

- **Security boundary.** Actions that require gating are natural candidates. Reversibility is a useful criterion: hard-to-reverse actions (external API calls, sending messages, deleting data) can be gated behind user confirmation. A `send_email` tool is easy to gate; `bash -c "curl -X POST ..."` is not.
- **Staleness checks.** A dedicated `edit` tool can reject writes if the file changed since Claude last read it. Bash can't enforce that invariant.
- **Rendering.** Some actions benefit from custom UI. Claude Code promotes question-asking to a tool so it can render as a modal, present options, and block the agent loop until answered.
- **Scheduling.** Read-only tools like `glob` and `grep` can be marked parallel-safe. When the same actions run through bash, the harness can't tell a parallel-safe `grep` from a parallel-unsafe `git push`, so it must serialize.

**Rule of thumb:** Start with bash for breadth. Promote to dedicated tools when you need to gate, render, audit, or parallelize the action.

---

## Anthropic-Provided Tools

| Tool | Side | When to use it | What to expect |
| --- | --- | --- | --- |
| **Bash** | Client | Claude needs to execute shell commands. | Claude emits commands; your harness executes them. Reference implementation provided. |
| **Text editor** | Client | Claude needs to read or edit files. | Claude views, creates, and edits files via your implementation. Reference implementation provided. |
| **Computer use** | Client or Server | Claude needs to interact with GUIs, web apps, or visual interfaces. | Claude takes screenshots and issues mouse/keyboard commands. Can be self-hosted (you run the environment) or Anthropic-hosted. |
| **Code execution** | Server | Claude needs to run code in a sandbox you don't want to manage. | Anthropic-hosted container with built-in file and bash sub-tools. No client-side execution. |
| **Web search / fetch** | Server | Claude needs information past its training cutoff (news, current events, recent docs) or the content of a specific URL. | Claude issues a query or URL; Anthropic executes it and returns results with citations. |
| **Memory** | Client | Claude needs to save context across sessions. | Claude reads/writes a `/memories` directory. You implement the storage backend. |

**Client-side** tools are defined by Anthropic (name, schema, Claude's usage pattern) but executed by your harness. Anthropic provides reference implementations. **Server-side** tools run entirely on Anthropic infrastructure - declare them in `tools` and Claude handles the rest.

---

## Composing Tool Calls: Programmatic Tool Calling

With standard tool use, each tool call is a round trip: Claude calls the tool, the result lands in Claude's context, Claude reasons about it, then calls the next tool. Three sequential actions (read profile -> look up orders -> check inventory) means three round trips. Each adds latency and tokens, and most of the intermediate data is never needed again.

**Programmatic tool calling (PTC)** lets Claude compose those calls into a script instead. The script runs in the code execution container. When the script calls a tool, the container pauses, the call is executed (client-side or server-side), and the result returns to the running code - not to Claude's context. The script processes it with normal control flow (loops, filters, branches). Only the script's final output returns to Claude.

| When to use it | What to expect |
| --- | --- |
| Many sequential tool calls, or large intermediate results you want filtered before they hit the context window. | Claude writes code that invokes tools as functions. Runs in the code execution container. Token cost scales with final output, not intermediate results. |

---

## Scaling the Tool and Instruction Set

| Feature | When to use it | What to expect |
| --- | --- | --- |
| **Tool search** | Many tools available, but only a few relevant per request. Don't want all schemas in context upfront. | Claude searches the tool set and loads only relevant schemas. Tool definitions are appended, not swapped - preserves cache (see Caching below). |
| **Skills** | Task-specific instructions Claude should load only when relevant. | Each skill is a folder with a `SKILL.md`. The skill's description sits in context by default; Claude reads the full file when the task calls for it. |

Both patterns keep the fixed context small and load detail on demand.

---

## Long-Running Agents: Managing Context

| Pattern | When to use it | What to expect |
| --- | --- | --- |
| **Context editing** | Context grows stale over many turns (old tool results, completed thinking). | Tool results and thinking blocks are cleared based on configurable thresholds. Keeps the transcript lean without summarizing. |
| **Compaction** | Conversation likely to reach or exceed the context window limit. | Earlier context is summarized into a compaction block server-side. See `SKILL.md` §Compaction for the critical `response.content` handling. |
| **Memory** | State must persist across sessions (not just within one conversation). | Claude reads/writes files in a memory directory. Survives process restarts. |

**Choosing between them:** Context editing and compaction operate within a session - editing prunes stale turns, compaction summarizes when you're near the limit. Memory is for cross-session persistence. Many long-running agents use all three.

---

## Caching for Agents

**Read `prompt-caching.md` first.** It covers the prefix-match invariant, breakpoint placement, the silent-invalidator audit, and why changing tools or models mid-session breaks the cache. This section covers only the agent-specific workarounds for those constraints.

| Constraint (from `prompt-caching.md`) | Agent-specific workaround |
| --- | --- |
| Editing the system prompt mid-session invalidates the cache. | Append a `{"role": "system", ...}` message to `messages[]` instead (no beta header; on supporting models - see `prompt-caching.md` § Mid-conversation system messages). The cached prefix stays intact, and the model treats it as an operator-authority instruction rather than user text. On models that don't support it, fall back to a `<system-reminder>` text block in the user turn. |
| Switching models mid-session invalidates the cache. | Spawn a **subagent** with the cheaper model for the sub-task; keep the main loop on one model. On Managed Agents that is a `multiagent` roster entry - see `managed-agents-multiagent.md`. |
| Adding/removing tools mid-session invalidates the cache. | Use **tool search** for dynamic discovery - it appends tool schemas rather than swapping them, so the existing prefix is preserved. |

For multi-turn breakpoint placement, use the combination in `prompt-caching.md` § Automatic vs explicit breakpoints: one explicit breakpoint on the static system prefix plus top-level automatic caching for the conversation tail (where automatic caching is available).

---

For live documentation on any of these features, see `live-sources.md`.

```

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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