The Complete Guide to Building Agents with the Claude Agent SDK
A tutorial by nader dabit. Featured in the OTF curated resource library.
What Is the Claude Agent SDK?
The Claude Agent SDK is Anthropic's framework for building autonomous AI agents that can reason, use tools, and complete multi-step tasks. Unlike simple chat interactions, agents can plan, execute, observe results, and iterate — creating a loop that handles complex workflows without constant human input.
The SDK provides primitives for:
- Tool definitions — declaring what actions the agent can take
- Reasoning loops — the agent decides which tool to use, executes it, and processes the result
- Memory — maintaining context across multiple turns and sessions
- Streaming — real-time output for responsive user experiences
Think of it as the difference between a calculator (you press buttons, it computes) and a researcher (you ask a question, they plan an approach, gather data, analyze, and present findings).
Core Concepts
Understanding these core concepts is essential before building your first agent.
The Agent Loop
An agent operates in a loop: receive a task → reason about the approach → select a tool → execute the tool → observe the result → decide if the task is complete or if another step is needed. This loop runs until the task is done or a maximum step count is reached.
Tool Definitions
Tools are functions the agent can call. Each tool has a name, description (so the agent knows when to use it), and a schema for its parameters. Good tool descriptions are crucial — they're the agent's instruction manual.
const searchTool = {
name: 'web_search',
description: 'Search the web for current information. Use when the user asks about recent events, current prices, or anything requiring up-to-date data.',
parameters: {
type: 'object',
properties: {
query: { type: 'string', description: 'The search query' }
},
required: ['query']
}
}System Prompts for Agents
An agent's system prompt defines its personality, capabilities, and constraints. Unlike chatbot system prompts, agent prompts should describe the agent's role, available tools, and decision-making framework. Be explicit about when to use which tool.
Streaming and Observability
Agents can take seconds or minutes to complete a task. Streaming lets users see the agent's reasoning process in real-time — which tool it's choosing, what it's thinking, and what results it's getting. This transparency builds trust.
Building Your First Agent
Let's build a practical agent: a research assistant that can search the web, read articles, and synthesize information.
Step 1: Define the tools. The agent needs: web_search (find relevant pages), read_page (extract content from a URL), and synthesize (compile findings into a coherent summary).
Step 2: Write the system prompt. 'You are a research assistant. When asked a question, search the web for relevant sources, read the most promising results, and synthesize a well-sourced answer. Always cite your sources.'
Step 3: Implement the loop. For each user query, the agent runs: search → read top 3 results → synthesize findings → return answer with citations. The SDK handles the loop mechanics — you just define the tools and the agent figures out the sequence.
Step 4: Add streaming. Show the user what the agent is doing in real-time: 'Searching for...' → 'Reading article from...' → 'Synthesizing findings...' → Final answer. This keeps users engaged during longer research tasks.
Tool Use Patterns
Sequential Tool Chains
Agent calls tools in sequence where each step depends on the previous result. Example: search → read → analyze → summarize. Each tool's output feeds into the next decision.
Parallel Tool Execution
When multiple independent operations are needed, the agent can request them simultaneously. Example: searching three different sources at once, then synthesizing all results together.
Conditional Branching
The agent makes decisions based on tool results. If a search returns no results, try a different query. If an API returns an error, fall back to an alternative data source. This adaptive behavior is what makes agents powerful.
Tool Composition
Complex workflows emerge from simple tools. A 'generate report' task might involve: querying a database, creating charts, writing analysis, formatting as PDF, and sending via email — each a simple tool, combined into a sophisticated workflow.
Production Deployment Patterns
Rate Limiting and Cost Control
Agents can make many API calls in a single task. Implement per-user rate limits, maximum step counts, and cost tracking. Set a hard limit on how many tool calls an agent can make per request.
Error Handling and Retries
Tools fail — APIs timeout, services go down. Implement automatic retries with exponential backoff, and teach the agent to handle tool failures gracefully in its system prompt.
Logging and Observability
Log every tool call, its parameters, the result, and the agent's reasoning. This trail is essential for debugging, cost optimization, and understanding agent behavior at scale.
Guardrails and Safety
Restrict which tools the agent can access based on user permissions. Validate tool parameters before execution. Implement output filtering for sensitive data. Never let agents access production databases without read-only restrictions.
