Software development today can feel like you’re trying to solve a puzzle while someone keeps adding new pieces. The tools are always changing, and the complexity just keeps piling up. For a while, AI felt like a single, smart assistant who could help with one thing at a time. But what if you could have a whole crew of specialized AI helpers?
That’s the idea behind Anthropic’s Claude Code subagents. It’s a feature that lets you build your own team of task-specific AI assistants right in your terminal. This shifts the model from a single co-pilot to an entire AI-powered team you can direct.
In this article, we’ll walk through 7 practical Claude Code subagents you can build today. We’ll also get real about when a custom-coded solution like this is the right move, and when a ready-made platform is a much better fit for your business teams.
First, what exactly are Claude Code subagents?
Claude Code subagents are specialized AI assistants you create and call on within a Claude Code session to handle specific jobs. They’re not just a slightly different prompt; they are distinct helpers with their own unique setups.
Based on Anthropic’s documentation, here’s what makes them different:
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They have their own context window. This is a big deal. It means a subagent’s work doesn’t clutter or confuse your main conversation, letting your primary agent stay focused on the bigger picture.
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They use custom system prompts. You get to define a subagent’s exact personality, expertise, and instructions. You can tell one to be a picky code reviewer and another to be a creative test case generator.
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They have scoped tool access. You can give a subagent access to only the tools it needs for its job. This is great for security and helps the agent focus on the task without getting distracted by other commands.
Think of it like being a project manager (the main agent) who delegates tasks to specialists on your team (the subagents). You wouldn’t ask your designer to run security audits or your QA tester to write marketing copy. Subagents let you apply that same logic to your AI workflow.
How we picked the best Claude Code subagents examples for this list
To make sure this list was useful and not just a bunch of neat ideas, we chose these examples based on a few simple questions:
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Does it solve a real, everyday problem for developers?
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Can it be used over and over on different projects?
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Is its purpose clear and focused?
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Is it something a developer could realistically build and start using without a massive time sink?
We focused on subagents that tackle the repetitive, time-consuming, or mentally draining parts of the job.
A quick comparison of our top Claude Code subagents picks
Here’s a high-level look at the seven specialized agents we’re about to dig into.
| Subagent Use Case | Primary Benefit | Technical Skill Required | Best For |
|---|---|---|---|
| Code Reviewer | Enforces quality and standards automatically. | Medium | Teams wanting consistent code quality. |
| Debugging Specialist | Speeds up root cause analysis. | Medium | Finding and fixing complex bugs faster. |
| Test Automation Expert | Generates and runs tests based on code changes. | High | Implementing test-driven development (TDD). |
| Documentation Writer | Keeps documentation in sync with code. | Low | Projects where good docs are essential. |
| Data Analyst | Queries and summarizes data insights. | Medium | Analyzing logs or database information. |
| Knowledge Base Agent | Answers questions based on a specific codebase. | Medium | Onboarding new developers to a project. |
| Security Auditor | Scans for common vulnerabilities. | High | Maintaining secure and compliant code. |
7 powerful Claude Code subagents you can build in 2025
Alright, let’s get into the details. Each of these subagents can be created with a simple Markdown file, turning Claude Code into a handy command center for your development workflow.
1. The code reviewer Claude Code subagent
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What it is: An automated specialist that checks code changes against a predefined list for quality, security, and style. You can teach it your team’s specific style guides and best practices.
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Why it’s useful: It frees up a ton of time for senior developers who would otherwise be stuck doing routine reviews. It applies the same standards to everyone and catches common slip-ups before they get merged.
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Example Prompt Snippet: You can set it up with a Markdown file that defines its entire personality and checklist.
"`markdown
name: code-reviewer
description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.
tools: Read, Grep, Glob, Bash
You are a senior code reviewer ensuring high standards of code quality and security.
When invoked:
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Run git diff to see recent changes
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Focus on modified files
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Begin review immediately
Review checklist:
– Code is simple and readable
– Functions and variables are well-named
– No duplicated code
– Proper error handling
– No exposed secrets or API keys
Provide feedback organized by priority: Critical, Warnings, and Suggestions.
"`
Just as this agent automates code review for developers, other tools can automate review for different teams. For instance, eesel AI’s AI Triage can automatically look over, tag, and route incoming support tickets, making sure they land in the right place before a human agent even has to look at them.
2. The debugging specialist Claude Code subagent
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What it is: An expert debugger that systematically goes through error messages, stack traces, and logs to find the root cause of a problem.
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Why it’s useful: It turns troubleshooting from a frustrating guessing game into a methodical process. This agent can cut down the time you spend just figuring out why something is broken, so you can get to the fix faster.
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Example Prompt Snippet: This configuration primes the agent to be an expert in root cause analysis.
"`markdown
name: debugger
description: Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
tools: Read, Edit, Bash, Grep, Glob
You are an expert debugger specializing in root cause analysis.
When invoked:
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Capture error message and stack trace
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Identify reproduction steps
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Isolate the failure location
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Form and test hypotheses
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Implement a minimal fix and verify it works
Focus on fixing the underlying issue, not just the symptoms.
"`
3. The test automation expert Claude Code subagent
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What it is: A subagent designed to be your partner in test-driven development (TDD). It can read a new function or feature, then write, run, and even help fix the tests for it.
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Why it’s useful: It makes TDD feel less like a chore. By handling the boilerplate of test writing, it helps your team maintain good test coverage, which leads to more stable software down the road.
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Example Prompt Snippet: You could create a custom prompt to guide its test creation process.
"`markdown
name: tdd-assistant
description: Writes unit tests for new code.
tools: Read, Write, Bash
You are a test automation expert. Your goal is to write a comprehensive unit test for the provided code snippet.
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Analyze the function’s inputs, outputs, and edge cases.
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Write a new test file using the project’s testing framework (e.g., Jest, Pytest).
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Cover the happy path and at least two edge cases (e.g., null inputs, empty arrays).
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Run the test to confirm it fails initially, then wait for the implementation.
"`
4. The documentation writer Claude Code subagent
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What it is: A subagent that watches for code changes and automatically updates the relevant docs, like your project’s README, API specs, or even inline comments.
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Why it’s useful: Let’s be honest, keeping documentation in sync with the code is a pain. This agent helps solve that by making documentation updates a natural part of the development process, so your docs are never out of date.
5. The data analyst Claude Code subagent
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What it is: A specialist for digging into data. It can run SQL queries, parse log files, analyze CSVs, and give you a summary of what it finds in plain English.
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Why it’s useful: It gives you data analysis powers without making you leave your terminal. You can quickly ask about user behavior, performance metrics, or error trends without having to switch contexts and open another tool.
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Example Prompt Snippet: This agent is configured to be an expert in SQL and BigQuery.
"`markdown
name: data-scientist
description: Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
tools: Bash, Read, Write
You are a data scientist specializing in SQL and BigQuery analysis.
When invoked:
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Understand the data analysis requirement.
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Write efficient, optimized SQL queries.
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Analyze and summarize the results clearly.
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Present findings with data-driven recommendations.
"`
6. The knowledge base Claude Code subagent
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What it is: An internal Q&A bot that knows a specific codebase inside and out. Team members can ask it questions about architecture, why a function exists, or project history.
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Why it’s useful: This is a fantastic tool for getting new developers up to speed. Instead of interrupting a senior engineer, a new hire can ask the agent, "How does our authentication system work?" and get a detailed answer with links to the right files. It makes project knowledge accessible to everyone.
While this is great for a codebase, almost every business team needs the same thing for their internal knowledge. eesel AI’s Internal Chat provides this kind of functionality right out of the box. It connects to all your company’s knowledge sources like Confluence, Google Docs, and Slack to give any team member instant, expert answers, no coding required.
7. The security auditor Claude Code subagent
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What it is: A subagent that acts as a security watchdog. It scans your code for common vulnerabilities, like exposed API keys, hardcoded secrets, or potential SQL injection spots.
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Why it’s useful: It’s a solid first line of defense. By catching potential security flaws early in the development cycle, it helps prevent serious issues from ever making it to production.
Tips for getting the most out of your Claude Code subagents
Building your own AI assistants is fun, but it helps to have a little strategy. Here are a couple of tips to keep in mind.
Keep your Claude Code subagents focused on a single job
The best subagents are specialists, not generalists. It’s tempting to build one "super-agent" that tries to do everything, but it’s better to create focused agents that do one thing really well. A dedicated code-reviewer will always do a better job than an agent trying to review code, write tests, and update documentation. This makes them more reliable and easier to manage.
Be very clear in your Claude Code subagents prompts
The quality of your subagent’s work depends entirely on the quality of its system prompt. Don’t be vague. Your prompt should be a detailed instruction manual. Include step-by-step processes, rules ("don’t use external libraries"), and even examples of what to do and what to avoid. The more guidance you give, the better the results will be.
Know when to build versus buy your Claude Code subagents: The eesel AI alternative
Building and maintaining Claude Code subagents is a really cool capability for engineering teams. But it’s also an investment of time and resources. You have to write the prompts, manage the configurations, and keep everything updated.
So, what if your customer support, IT, or internal ops teams need this kind of specialized AI, but you don’t have developers to spare for a big project?
This is where a "buy" decision makes more sense. eesel AI is designed to give your business teams the power of specialized AI agents without the engineering overhead.
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Go live in minutes, not months: With eesel AI, you can connect your help desk and knowledge sources with a few clicks and have a working AI agent running the same day. You can get started right away without sitting through sales calls or demos.
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Total control without code: You don’t need to write Markdown files to define how the AI behaves. eesel AI gives you a simple prompt editor and a workflow engine that lets non-technical users customize the AI’s persona, decide what it can and can’t answer, and set up custom actions.
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Test with confidence: Building a subagent from scratch involves a lot of trial and error. With eesel AI, you can use its simulation mode to test your AI on thousands of past support tickets. You get a clear forecast of how it will perform and can adjust its behavior risk-free before a single customer ever talks to it.
[This video provides a great breakdown of what Claude Code’s new subagents feature is and how you can build an AI army in your terminal.]
Your Claude Code subagents team is ready, how will you use it?
Claude Code subagents are a really interesting step forward for coding with AI. They let developers move beyond a single assistant and build a custom team of AI specialists that fits their workflow perfectly. The possibilities are huge, from automating tedious tasks to speeding up complex problem-solving.
But it also highlights a clear choice. For developer-focused tasks where you need deep control over a codebase, building your own subagents is a fantastic option. However, for business workflows like customer service or internal Q&A, a ready-made platform is almost always faster, simpler, and easier to scale.
Ready to give your support team their own specialized AI agents in a matter of minutes? Start your free eesel AI trial and see how easy it is to get your workflows automated.
Frequently asked questions
The key difference is that they have their own isolated context windows and can be given specific, limited access to tools. This prevents them from cluttering your main conversation and makes them more focused and secure than just a simple prompt swap.
They do require some initial setup, like writing clear system prompts in Markdown files. The main effort is in refining these prompts over time to get the exact behavior you want, but once built, they are highly reusable.
The best practice is to use the principle of least privilege by scoping tool access tightly. Only grant a subagent the specific commands it absolutely needs, like Read and Grep for a reviewer, to minimize any potential risks.
A detailed prompt is great for one-off or simple tasks. You should build dedicated subagents for complex, repetitive workflows that benefit from their own context or specific tools, like an automated test writer or a security auditor.
Absolutely. You can store the Markdown configuration files for your subagents in a shared team repository. This allows everyone to pull the latest versions and use the same specialized assistants, ensuring consistency across your team’s workflow.
