How to build specialist AI subagents that replace entire development teams

Most developers try to make Claude do everything at once. They throw massive requirements at it, watch it lose context halfway through, and then wonder why the output is mediocre. But there's a better way.

Sub‑agents are specialized mini‑agents you define (with their own system prompt, tool permissions, and an independent context window) that the main agent can delegate to for specific tasks (e.g., code‑reviewer, test‑runner, debugger). Think of them as your personal development team where each member has a specific role and expertise.

In this guide, you'll learn how to build a complete engineering ecosystem using Claude's subagents, from planning to deployment, with each agent specialized for maximum efficiency.

What makes subagents different from regular prompting?

The key insight is context isolation. If you asked a single AI agent to perform a complex, multi-stage task, it would exhaust its context window and start losing crucial details. By using subagents you give each specialist its own dedicated context window, ensuring the quality of each step is preserved.

The Agent SDK gives you the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript. The Claude Agent SDK provides the framework to build these specialized agents programmatically.

Here's how it works: instead of one agent juggling planning, coding, testing, and deployment, you create specialists. The product manager agent focuses entirely on requirements. The architect reviews technical feasibility. The engineer writes clean code. Each operates with fresh context and domain expertise.

Why does this approach actually work?

Traditional agent workflows fail because of context pollution. A code‑review agent won't "forget" it's reviewing while your main thread stays focused on product goals. With subagents, your main conversation stays focused while specialists handle complex subtasks.

Give them roles: Product Spec, Architect, Implementer/Tester, and chain them with Claude Code hooks to create a dependable software pipeline: Reproducibility: Stop re-prompting. Subagents and hooks codify repeatable steps. Separation of concerns: PM asks, Architect validates, Implementer builds & tests, QA verifies. Governance & safety: Each agent has scoped tools & permissions, while hooks gate and log transitions.

The Claude Code documentation shows how to create custom subagents with specific tools, permissions, and system prompts. This isn't just automation—it's architectural thinking applied to AI workflows.

How do you set up your first subagent team?

Start with the Claude Agent SDK quickstart to get the basic framework running. You'll need Claude Code CLI installed and an API key configured.

Subagents are defined in Markdown files with YAML frontmatter. You can create them manually or use the /agents command. Here's the simplest way to get started:

1. Run claude in your terminal to start a Claude Code session
2. Type /agents to open the subagent management interface
3. Choose "Create new agent" and select whether it's user-level (available everywhere) or project-specific

Select Generate with Claude. When prompted, describe the subagent: ... A code improvement agent that scans files and suggests improvements for readability, performance, and best practices.

The key is being specific about the agent's role and constraints. Don't just say "help with coding"—define exactly what this agent should do and when Claude should delegate to it.

What tools should each specialist have access to?

Tool permissions are critical for subagent effectiveness. Each subagent's tools field specifies Claude Code built-in tools, optimized for their role: Read-only agents (reviewers, auditors): Read, Grep, Glob - analyze without modifying · Research agents (analysts, researchers): Read, Grep, Glob, WebFetch, WebSearch - gather information · Code writers (developers, engineers): Read, Write, Edit, Bash, Glob, Grep - create and execute · Documentation agents (writers, documenters): Read, Write, Edit, Glob, Grep, WebFetch, WebSearch - document with research

Here's a practical example for a code reviewer subagent:

---
name: code-reviewer
description: Reviews code for bugs, security issues, and best practices
tools: Read, Grep, Glob
model: sonnet
---

You are a senior code reviewer focused on identifying potential issues.
Look for:
- Security vulnerabilities
- Performance bottlenecks  
- Code maintainability issues
- Missing error handling

Notice how the reviewer only gets read access. This prevents it from accidentally modifying code while doing analysis.

How do you create a complete development pipeline?

I ran a single custom command to generate a ticket for the new page. This command invoked several specialist sub-agents (you can find their .md definitions in the appendix)—a product-manager, a ux-designer, and a senior-software-engineer—who worked in parallel to flesh out the requirements.

The real power comes from chaining subagents together. Create these core specialists:

Product Manager Agent: Analyzes requirements, defines user stories, creates acceptance criteria

• Tools: Read, WebSearch, WebFetch (for research)
• Focus: Business logic and user needs

Technical Architect Agent: Reviews technical feasibility, suggests implementation approach

• Tools: Read, Grep, Glob (to understand existing codebase)
• Focus: System design and technical constraints

Senior Engineer Agent: Implements features following architectural guidelines

• Tools: Read, Write, Edit, Bash, Glob, Grep
• Focus: Clean code implementation

QA Tester Agent: Creates test cases, identifies edge cases

• Tools: Read, Write, Bash (to run tests)
• Focus: Quality assurance and test coverage

You can find community-created subagent templates at the Awesome Claude Code Subagents repository.

What are the advanced patterns for subagent orchestration?

Slash commands can also orchestrate other behavior: you can spell out in the command itself that it should spin up a subagent (or a specific subagent), call out a particular skill/workflow, and generally "pipeline" the work (e.g., research → codebase scan → write a doc) instead of trying to do everything in one shot.

Create custom slash commands that orchestrate multiple subagents:

---
name: full-feature
description: Complete feature development from requirements to testing
---

Execute the full feature development pipeline:

1. 👨‍💼 Product Manager, analyze the requirements and create user stories
2. 🏗️ Technical Architect, review technical feasibility and suggest approach  
3. 👨‍💻 Senior Engineer, implement the feature following the architectural plan
4. 🧪 QA Tester, create comprehensive test cases and verify implementation

Each agent should provide structured output for the next agent to consume.

Claude Code also supports async agents: fire one off, let it cook while you keep working, then it comes back with its updates when it's done. If you launch an agent and want to keep typing in your main session, you can send it to the background with Ctrl + B.

This means you can start a research agent analyzing your codebase while you work on something else, then integrate its findings later.

How do you avoid common subagent pitfalls?

Even though subagents are specialized and generally perform better for domain-specific tasks than stock Claude, they'll still have their blind spots and weaknesses. Engineers have reported strong results when including these weaknesses in their subagent system prompts.

Key mistakes to avoid:

Over-specialization: Don't create a subagent for every tiny task. You'll probably max out at about 3 or 4 subagents total; after that your own productivity may drop.

Vague descriptions: Claude decides when to delegate based on the description. Be specific about when this agent should be used.

Wrong tool permissions: Match tools to the agent's actual needs. A reviewer doesn't need write access.

Missing context handoffs: Ensure each agent provides structured output that the next agent can consume.

In your system prompt, it helps to instruct your agent to "be honest" or "be critical" or "be realistic". Many LLM system prompts default to an agreeable demeanor, so you'll want to be sure yours overrides this.

What's the real productivity impact?

In our intro story, the product-manager was able to use its entire 200k context to focus only on user needs and business logic. The senior-software-engineer then received the final ticket and could use its own fresh 200k context to focus only on implementation, without needing to remember the nuances of the initial product discussion. This prevents quality degradation.

Teams report significant improvements in code quality and development speed. Instead of re-prompting the same agent multiple times as it loses context, you get consistent, specialized output from each team member.

The Claude Agent SDK documentation provides deployment patterns for production environments, including Docker containerization and session management.

Companies like PubNub are already using this approach: At PubNub, we are migrating from ad-hoc prompts to a subagent pipeline that designs features, reviews architecture, implements code, runs tests, and hands back clean PRs, repeatably and safely.

The future of development isn't about replacing developers—it's about augmenting them with specialized AI teammates that handle routine tasks while humans focus on architecture, creative problem-solving, and business strategy. Subagents make this vision practical today.

Start with one specialized subagent for your most repetitive task. As you see the productivity gains, expand your team. Before long, you'll have a complete AI-powered development pipeline that maintains context, follows best practices, and delivers consistent results.