Building AI Agents with Claude Code: The Practical Guide

What Are Claude Code Agents?

Claude Code agents are autonomous AI workers that can perform complex tasks by reading code, writing files, running commands, and coordinating with other agents. Unlike a simple chat-based assistant that responds to one question at a time, an agent takes a high-level objective and breaks it down into steps, executes those steps, handles errors, and verifies the result -- all with minimal human intervention.

At the simplest level, every time you use Claude Code, you are already interacting with an agent. When you say "refactor the authentication module to use JWT tokens," Claude Code acts as an agent that reads the current implementation, plans the changes, edits multiple files, updates tests, and runs them to verify correctness.

But the real power emerges when you compose multiple agents into workflows, use subagents for parallel execution, and integrate agents into your CI/CD pipeline for automated code maintenance.

Agent Architecture

Claude Code's agent architecture is built around three key concepts: the main agent, subagents, and orchestration.

The Main Agent

The main agent is the Claude Code instance you interact with directly. It has full access to your project, can read and write files, execute commands, and use any configured MCP servers. The main agent is responsible for understanding your request, planning the approach, and deciding whether to handle the work directly or delegate to subagents.

Subagents

Subagents are separate Claude Code instances spawned by the main agent to handle specific subtasks. Each subagent has its own context window and can work independently. This is powerful for two reasons: it allows parallel execution of independent tasks, and it gives each subtask a clean context focused on just that piece of work.

For example, when building a new feature, the main agent might spawn three subagents: one to build the API endpoint, one to create the frontend component, and one to write the integration tests. Each works independently, and the main agent coordinates the results.

Orchestration

Orchestration is the pattern of coordinating multiple agents to accomplish a larger goal. The main agent acts as the orchestrator, deciding how to decompose a task, which subagents to spawn, what information each needs, and how to merge their results. Good orchestration means clear task boundaries, well-defined interfaces between agents, and explicit success criteria.

Building Your First Agent

The simplest way to build an agent is using Claude Code's headless mode with the -p flag. This lets you send a task to Claude Code programmatically and receive the result without interactive conversation.

Example: Code Review Agent

Here is a simple agent that reviews code changes and posts feedback. You can run this as part of a GitHub Action or any CI pipeline:

#!/bin/bash
# code-review-agent.sh

# Get the diff of changes
DIFF=$(git diff main...HEAD)

# Run Claude Code as an agent
claude -p "Review the following code changes. Focus on:
1. Security vulnerabilities
2. Performance issues
3. Code style violations
4. Missing error handling
5. Test coverage gaps

Provide specific, actionable feedback with file names
and line numbers.

Changes:
$DIFF"

Example: Documentation Agent

This agent scans your codebase and generates or updates documentation:

#!/bin/bash
# docs-agent.sh

claude -p "Scan the src/api/ directory and generate
API documentation in Markdown format. For each endpoint:
- HTTP method and path
- Request parameters and body schema
- Response format with examples
- Authentication requirements
- Error codes

Save the output to docs/api-reference.md"

Example: Using the SDK

For more control, use the Claude Code SDK (TypeScript) to build agents programmatically:

import { Claude } from "@anthropic-ai/claude-code";

const claude = new Claude();

// Run a task and get the result
const result = await claude.run({
  prompt: "Find and fix all TypeScript type errors in src/",
  workingDirectory: "./my-project",
  maxTurns: 20,
});

console.log(result.output);
console.log("Files modified:", result.filesModified);

Multi-Agent Workflows

Multi-agent workflows use several agents working together to accomplish tasks that are too complex or too broad for a single agent. The key patterns are sequential pipelines, parallel fan-out, and supervisor hierarchies.

Sequential Pipeline

In a sequential pipeline, each agent's output feeds into the next agent's input. This is useful for workflows with clear stages:

# Sequential pipeline: Plan → Implement → Test → Review

# Stage 1: Planning agent creates a spec
claude -p "Analyze the requirements in SPEC.md and create
a detailed implementation plan" > plan.md

# Stage 2: Implementation agent builds the feature
claude -p "Implement the feature described in plan.md"

# Stage 3: Testing agent writes and runs tests
claude -p "Write comprehensive tests for the changes made
in the last commit and run them"

# Stage 4: Review agent checks quality
claude -p "Review all changes since main for code quality,
security, and adherence to our coding standards"

Parallel Fan-Out

When tasks are independent, you can run multiple agents simultaneously. Claude Code supports this natively through its subagent system, or you can orchestrate it externally:

# Parallel fan-out: run independent tasks simultaneously

claude -p "Build the REST API for user management" &
claude -p "Create the React dashboard components" &
claude -p "Write the database migration scripts" &

# Wait for all agents to finish
wait

# Coordinator: integrate the results
claude -p "Review all changes made in the last 3 commits
and ensure the API, frontend, and database layers are
properly integrated"

Supervisor Hierarchy

In a supervisor hierarchy, a lead agent decomposes a complex task and delegates subtasks to worker agents. The supervisor monitors progress, handles failures, and merges results. This is the most sophisticated pattern and works well for large-scale projects.

Claude Code's built-in subagent dispatching handles supervisor hierarchies automatically. When you give Claude Code a large task, it can decide on its own to spawn subagents for independent pieces of work, then coordinate the results.

Real-World Use Cases

Best Agent Resources on Claude 4 World

The Claude 4 World ecosystem includes a growing collection of agent templates, orchestration patterns, and ready-to-use agent configurations. Here are the categories worth exploring:

• --Agent Templates: Pre-built agent configurations for common workflows like code review, documentation generation, and test writing.
• --Orchestration Prompts: Carefully designed prompts that help the main agent decompose tasks and coordinate subagents effectively.
• --CI/CD Integrations: GitHub Actions, GitLab CI templates, and pipeline configurations for running Claude Code agents in automated workflows.
• --Multi-Agent Examples: Complete working examples of multi-agent systems for different scales and use cases.

Browse the full collection of Claude Code prompts and tools to find agent patterns that match your workflow.

Frequently Asked Questions