Building Agents with the Claude Agent SDK

The Claude Agent SDK turns a large language model like Claude into a tool-using software agent. It extends the model’s reasoning with a computer-like environment — permission to run code, search files, and orchestrate multi-step work. It was built for Claude Code and is now a general-purpose foundation for agentic design.

The rest of this guide covers the patterns that make such agents reliable: the feedback loop, subagents, tool design, and verification.

The agentic feedback loop

Every Claude agent runs a closed loop that pairs action with a check, which minimizes errors and drives self-correction:

Gather context → Take action → Verify work → Repeat

The agent stays in the loop until it reaches a verifiable outcome that matches the task.

Context management: the agent’s computer

Autonomy comes from giving Claude system-level tools and a place to keep state.

• File system access — read, create, edit, and organize files, giving the agent a form of persistent memory.
• Context folders — structured directories (conversations/, logs/) act as dedicated knowledge stores.
• Agentic search — utilities like grep and tail scan large files transparently, often more precisely than semantic search for locating specific data.

Subagents: parallelism and isolation

Subagents are lightweight child processes a parent launches for a specific, isolated task. They offer two things:

1. Parallelism — run several search, analysis, or processing tasks at once.
2. Context isolation — each subagent has its own memory and returns only its result to the parent, which keeps the parent’s context window clean and focused.

This is the building block for larger multi-agent systems.

Tools and actions: Code, Bash, and MCP

Agents act through explicitly defined tools registered in the SDK.

• Custom tools — atomic, single-responsibility functions with clear docs and input validation.
• Bash and scripting — sandboxed system commands (grep, python, and the like) for parsing, testing, and file transformations.
• Code generation — having the agent generate and run code (Python, SQL) makes its actions deterministic, auditable, and verifiable through return codes and assertions.
• Model Context Protocol (MCP) — standardized connectors to external systems (Slack, GitHub, Asana) that handle auth and API calls, so complex services drop in as simple tools.

Verification loops

Verification closes the loop and lets an agent detect and repair its own mistakes.

• Rule-based evaluation — validation logic (like linting) checks output against a schema or format; on failure the agent gets specific error feedback and self-corrects.
• Visual feedback — for UI work, an agent can render HTML with a tool like Playwright, screenshot it, and compare against the target.
• LLM-as-judge — a secondary agent reviews qualitative aspects such as tone, style, or reasoning integrity.

Implementation practices

Getting started

1. Install: pip install claude-agent-sdk
2. Define tools and memory — register the agent’s capabilities and persistent storage.
3. Implement the loop — structure logic around Gather → Act → Verify.
4. Add MCP integrations — connect services like GitHub or Slack.
5. Iterate and evaluate — use automated tests and logs to monitor and improve.

See also

• Introduction to AI Agents
• Claude
• Agentic Context Engineering