Getting Started with the Claude Code SDK to Orchestrate Multiple AI Instances

Why Bother with Multi-Agent Orchestration?

• Parallel Development: This is the big one. You can have different agents working on different parts of your application simultaneously. Imagine a frontend agent, a backend agent, & a testing agent all working in parallel. The speed increase is just phenomenal – some people have reported up to 4x faster progress.
• Built-in Quality Checks: With multiple agents, you can have one agent write the code & another review it. This creates a natural system of checks & balances, leading to higher-quality code. Each agent brings a different "perspective" to the problem, catching things that a single agent might miss.
• Clear Separation of Concerns: Just like in a human development team, you can assign specific roles to each agent. One might be the "Architect," responsible for planning & high-level design. Another could be the "Builder," focused on implementation, while a "Validator" handles testing & debugging. This keeps everything organized & manageable.
• Enhanced Specialization: Each AI agent can be fine-tuned for a specific domain. You can have an agent that's an expert in database design, another that's a master of UI/UX, & a third that's a wizard with cloud infrastructure. This leads to more focused & consistent results.
• Scalability: Need to tackle a more complex project? Just add more agents to your team. A multi-agent system can easily scale up as your needs grow, without overloading a single AI.

The Core of It All: The Claude Code SDK

• It’s Production-Ready: The SDK comes with all the stuff you need for real-world applications, like built-in error handling, session management, & monitoring.
• Rich Tool Ecosystem: It gives your agents access to a wide range of tools, including file operations, code execution, web search, & more. This is what allows them to actually do things in your environment.
• Fine-Grained Permissions: You have complete control over what your AI agents can & can't do. This is SUPER important for security & preventing any unintended consequences.
• Optimized Integration: The SDK is designed for performance, with features like automatic prompt caching to speed things up.
• Available in Your Favorite Languages: You can use the SDK from the command line, TypeScript, or Python, so it fits right into your existing workflow.

Getting Your Hands Dirty: A Practical Guide to Multi-Agent Orchestration

The Simple Multi-Terminal Approach

1. Prepare Your "Memory" Files: The key to this setup is using Claude's user memory feature to define the roles of your agents. You’ll create a template that outlines the different agent roles & their responsibilities. For example, you might have:
   • Agent 1 (Architect): Responsible for research, planning, & creating design documents.
   • Agent 2 (Builder): Focuses on writing the code based on the architect's plan.
   • Agent 3 (Validator): Handles testing, debugging, & ensuring the code meets the requirements.
   • Agent 4 (Scribe): Takes care of documentation & keeping everything organized.

2. Launch Your Agents: Open up your code editor (like VS Code) with four separate terminal tabs. In each terminal, you'll launch a Claude Code instance & assign it a role. It looks something like this:
   • Terminal 1:
     1 cd /your-project && claude > You are Agent 1 - The Architect.
   • Terminal 2:
     1 cd /your-project && claude > You are Agent 2 - The Builder.
   • Terminal 3:
     1 cd /your-project && claude > You are Agent 3 - The Validator.
   • Terminal 4:
     1 cd /your-project && claude > You are Agent 4 - The Scribe.
3. Use a Shared Planning Document: To keep all your agents on the same page, you'll use a shared planning document, like a

   1 MULTI_AGENT_PLAN.md

   file. The Architect will create the initial plan in this document, & the other agents will refer to it for their tasks.
4. Version Control is Your Best Friend: Use Git to keep everything organized. A great pro-tip is to use a separate branch for each agent, like

   1 agent1/planning

   ,

   1 agent2/implementation

   , etc. This helps to avoid conflicts & makes it easy to see what each agent is working on.

The More Advanced Sub-Agent Approach

1. Define Your Sub-Agents: You'll create configuration files for each of your sub-agents. In these files, you'll define their name, description, system prompt (which tells them how to behave), & the tools they have access to. For example, you could create sub-agents for:
   • Frontend Developer: Specializes in creating responsive UIs.
   • Backend Developer: Handles server-side logic & API development.
   • API Developer: Focuses on designing & building robust APIs.
   • Python Developer: An expert in all things Python.
2. Orchestrate with Slash Commands: One of the coolest ways to use sub-agents is with slash commands. You can create custom commands that trigger a specific workflow involving multiple sub-agents. For example, you could create a

   1 /deep-research

   command that:
   • First, calls a "Search Planner" sub-agent to break down your query into specific search tasks.
   • Next, it uses a "Search Orchestrator" sub-agent to execute the searches.
   • Finally, it calls a "Search Summarizer" sub-agent to summarize the results in a nice, clean format.

The Inevitable Challenges: What to Watch Out For

• Resource Contention & File Conflicts: When you have multiple agents working on the same codebase, they're going to step on each other's toes. They might try to edit the same file at the same time, leading to conflicts. This is why using Git branches for each agent is so important.
• Coordination Nightmares: Keeping all your agents in sync can be tough. Sometimes, one agent might get ahead of the others, or they might have different ideas about how to implement a feature. This is where a clear plan & regular sync points are essential.
• Observability: When you have 10+ agents running in parallel, it can be hard to keep track of what they're all doing. You'll need good observability tools to see what's happening under the hood.
• AI "Hallucinations" & Mistakes: Let's be honest, AI agents are not perfect. They can make mistakes, "hallucinate" package names that don't exist, or get stuck in a loop. You, the human, need to be there to supervise, catch these mistakes, & guide the agents back on track.
• The Need for Human Intervention: Despite all the hype, we're not at the point where you can just press a button & have a team of AI agents build a complete application for you. The reality is that human intervention is still essential. You need to be the puppet master, guiding the agents, making sure they're working together effectively, & validating their work.

Real-World Examples: What Can You Actually Build?

• Building a Full-Stack Web App: You can have a team of agents work together to build a complete web application from scratch. One agent could set up the backend API, another could build the frontend with a modern framework like Next.js, & a third could write the automated tests.
• Automating Code Reviews: You could create a GitHub Action that uses a Claude Code agent to automatically review pull requests. The agent could check for style violations, potential bugs, & even suggest improvements.
• Triaging GitHub Issues: Imagine an agent that can automatically read new GitHub issues, categorize them, & even attempt to fix them. This could be a HUGE time-saver for open-source maintainers.
• Creating a Personalized Customer Experience: Businesses are always looking for ways to improve their customer service. With a tool like Arsturn, they can build no-code AI chatbots trained on their own data. These chatbots can provide instant support, answer questions, & engage with website visitors 24/7. Now, imagine integrating this with a multi-agent system. A customer could interact with a friendly Arsturn chatbot on the front end, & if their issue is complex, the chatbot could trigger a backend process where a team of specialized AI agents work together to resolve the problem.
• Boosting Conversions with AI: In the world of e-commerce, every little bit of engagement matters. A business could use Arsturn to build a personalized shopping assistant. This AI chatbot could help customers find products, answer questions about sizing & availability, & even offer personalized recommendations. Behind the scenes, a multi-agent system could be analyzing customer behavior in real-time to provide the most relevant suggestions, ultimately boosting conversions.

My Final Thoughts