Sub-agents in Claude Code

Test Agents at scale with other Agents [open-source]

Traditional testing relies on fixed inputs and exact outputs. But agents speak in language, and there’s no single “correct” response. That’s why we test Agents using other Agents by simulating Users and Judges.

Here's the process with LangWatch Scenario framework (open-source):

• Define three Agents:

  • The Agent you want to test.

  • A User Simulator Agent that acts like a real user.

  • A Judge Agent for evaluation.

• Let your Agent and User Simulator Agent interact with each other.

• Evaluate the exchange using the Judge Agent based on the specified criteria.

The code depicts a simulation run:

Done!

Key features:

• Test Agent behavior by simulating users in different scenarios.

• Evaluate at any point of the conversation using multi-turn control.

• Integrate any Agent by implementing just one call() method.

• Combine with any LLM eval framework or custom evals.

Here’s the GitHub repo →

Here’s the documentation →

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Sub-agents in Claude Code

Claude Code subagents solved two of AI’s biggest problems:

• Large Context management

• Right tool selection

Making it the best AI coding assistant!

The video above explains how to build and use Sub-agents in Claude code.

What are subagents?

Subagents are like focused teammates for your IDE.

Each one:

• Has a specific purpose

• Uses a separate context window

• Can be limited to selected tools

• Follows a custom system prompt

It works independently and returns focused results.

Quick start:

1. Open the subagents UI: /agents

2. Create a new agent & select its scope

3. Define your agent

4. Save it to use later

Now let's build a team of sub-agents and see them in action.

Here are 4 agents we're building:

1. Code-reviewer

2. Debugger

3. Data-scientist

4. Web-researcher

Next, we'll see a detailed description for each, one by one…

Here's a blueprint to create a subagent.

Each has its own:

• context window

• system prompt

• tools and tasks

Just add them to .claude/agents/my_agent.md

Code reviewer

This subagent reviews your code for quality, security, and maintainability.

It can also do semantic search over the entire codebase using Claude Context MCP by Ziliz (open-source).

Debugger

This subagent traces stack errors, diagnoses issues, applies minimal fixes, and confirms resolution.

Data Scientist

This subagent writes SQL, runs BigQuery, uncovers patterns in any dataset, and analyzes trends.

Web researcher

This subagent connects to Firecrawl’s MCP server to scrape, summarize, and extract real-time info from the web.

Finally, when done, we ask our system to create a leaderboard for trending GitHub repos.

It created a real-time leaderboard in one shot and automatically handled:

• Web scraping logic

• Writing the code for ranking

• Creating the leaderboard

Some best practices based on our experience:

• Start with Claude-generated agents, then customize

• Focus each agent on a single task

• Use detailed system prompts with examples

• Restrict tool access for security

• Write precise, action-oriented descriptions for better routing

Thanks for reading!

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We have discussed several other topics (with implementations) that align with such topics.

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• Learn everything about MCPs in this ​crash course with 9 parts →​

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