Building AI Agents with Claude Code

Claude Code's most powerful capability isn't file editing or git integration - it's the ability to spawn specialized AI agents that work together to solve complex problems. This guide explores how to build multi-agent systems using the Task tool.

What you'll learn: How to use the Task tool to create subagents, coordinate multiple agents for complex workflows, and apply best practices for agent orchestration in production environments.

The Task Tool

The Task tool is Claude Code's mechanism for spawning subagents. Think of it as creating a new AI worker with a specific job description and its own clean context.

Spawning Subagents

When you call the Task tool, you create a completely independent agent with:

• Fresh context: No inherited conversation history
• Specialized instructions: Custom system prompts for specific tasks
• Full tool access: All file operations, searches, and commands
• Performance tracking: Logged execution time and token usage

# Example: Spawning a code-generator agent
# The Task tool spawns a new agent with custom instructions

Task(
  instruction="Implement user authentication with JWT tokens",
  agent_name="code-generator",
  context={
    "spec_file": "/path/to/auth-spec.md",
    "test_requirements": "Unit and integration tests required"
  }
)

When to Delegate

Not every task needs a subagent. Delegate when:

• The task requires multiple file searches and analysis
• You need parallel execution of independent subtasks
• The work is specialized (testing, architecture, security review)
• You want performance isolation (separate timing and cost tracking)

"Use subagents for exploration, research, and complex refactoring. Keep simple file edits in the main context."

Subagent Types

Claude Code supports multiple specialized agent types. Each is optimized for specific tasks with tailored system prompts and quality gates.

CORE AGENTS:
├── orchestrator       # Multi-hour autonomous builds with quality gates
├── architect          # System design and technical planning
├── code-generator     # Feature implementation following specs
├── code-reviewer      # Quality assurance and security analysis
├── test-runner        # Automated testing with integration & E2E
└── integration-tester # API contract validation

PLATFORM AGENTS:
├── react-native-developer      # Cross-platform mobile
├── android-developer           # Android/Kotlin native
├── ios-developer              # iOS/Swift native
└── nodejs-typescript-backend  # Backend API development

SPECIALIZED AGENTS:
├── devops-agent      # Deployment and infrastructure
├── database-agent    # Schema design and migrations
├── documenter        # Documentation generation
└── agent-optimizer   # Performance analysis

Agent Selection Strategy:

• orchestrator: For multi-step workflows that take hours
• code-generator: When you have a clear spec and need implementation
• test-runner: For comprehensive test execution and reporting
• architect: When you need system design before implementation
• code-reviewer: For security audits and quality checks

Orchestration Patterns

Once you understand individual agents, the next step is coordinating multiple agents to solve complex problems.

Sequential Execution

Sequential patterns are ideal when each step depends on the previous one. Think of it as a pipeline where output from one agent becomes input for the next.

# Sequential Pattern: Each agent builds on the last

# Step 1: Architect designs the system
architect_output = Task(
    instruction="Design authentication system architecture",
    agent_name="architect"
)

# Step 2: Code generator implements the design
implementation = Task(
    instruction=f"Implement {architect_output.design_doc}",
    agent_name="code-generator"
)

# Step 3: Test runner validates the implementation
test_results = Task(
    instruction=f"Run full test suite for {implementation.files}",
    agent_name="test-runner"
)

# Step 4: Code reviewer checks quality
review = Task(
    instruction=f"Review code quality and security of {implementation.files}",
    agent_name="code-reviewer"
)

Use sequential execution when:

• Tasks have clear dependencies (design → implement → test → review)
• You need to pass data between agents
• The workflow has a logical order that can't be parallelized

Parallel Execution

Parallel patterns spawn multiple independent agents simultaneously. This dramatically reduces total execution time for independent tasks.

# Parallel Pattern: Multiple independent agents

# Spawn all agents at once (conceptually - actual parallel spawning
# depends on your orchestrator implementation)

agents = [
    Task(
        instruction="Implement user authentication API endpoints",
        agent_name="nodejs-typescript-backend"
    ),
    Task(
        instruction="Build React Native login screens",
        agent_name="react-native-developer"
    ),
    Task(
        instruction="Create PostgreSQL schema for users and sessions",
        agent_name="database-agent"
    ),
    Task(
        instruction="Set up Docker compose for local development",
        agent_name="devops-agent"
    )
]

# Wait for all to complete, then integrate
# (In practice, agents complete asynchronously and report back)

Use parallel execution when:

• Tasks are completely independent
• You need to minimize total execution time
• Different teams/components can work simultaneously

Best Practices

After building dozens of multi-agent systems, here are the non-obvious lessons:

1. Keep Agent Instructions Focused

Don't ask an agent to "research, design, implement, and test." That's four agents. Specialized agents produce better results because they have optimized prompts for their specific role.

❌ BAD: "Research authentication options, design the system,
        implement it, write tests, and deploy"

✅ GOOD: Spawn 4 agents:
   - architect: "Research and design authentication system"
   - code-generator: "Implement auth following design doc"
   - test-runner: "Execute full test suite"
   - devops-agent: "Deploy to staging environment"

2. Monitor Subagent Performance

Every subagent execution is logged to .claude/agent_performance.jsonl with timing, token usage, and file changes. Use this data to optimize your orchestration.

# View recent agent performance
.claude/hooks/view_agent_logs.sh

# Check which agents are slowest
cat .claude/agent_performance.jsonl | \
  jq -s 'sort_by(.duration_seconds) | reverse | .[0:5]'

3. Use Hooks for Agent Coordination

The SubagentStop hook runs after every subagent completes. Use it for notifications, logging, or triggering dependent agents.

4. Archive Agent Summaries

Subagent outputs are automatically archived in .claude/memory/subagents/. This preserves knowledge even after you compact your main context.

5. Quality Gates for Orchestrator

When using the orchestrator agent for multi-hour builds, it enforces these quality gates:

• Gate 1: Linting and code quality checks
• Gate 2: Integration tests (BLOCKING)
• Gate 3: E2E tests (BLOCKING)
• Gate 4: Build validation
• Gate 5: Security scan

Gates 2 and 3 are blocking - if they fail, the orchestrator stops and reports the failure instead of continuing with broken code.

Conclusion

AI agents in Claude Code transform how we build software. Instead of a single AI trying to do everything, you orchestrate specialized agents that excel at specific tasks.

The Task tool is your entry point to this world. Start with simple delegation, experiment with sequential and parallel patterns, and gradually build more sophisticated multi-agent systems.

"The future of coding isn't AI replacing developers - it's developers orchestrating teams of AI agents to build better software faster."