agent-design-patterns

name	agent-design-patterns
description	Reference guide covering decision heuristics for building agents on the Claude API, including tool surface design, context management, caching strategies, and composing tool calls
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.
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 top-level auto-caching — see prompt-caching.md §Placement patterns.

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