Custom AI Agents: Inside the Claude Code Workflow
In the fast-paced world of software development, efficiency and innovation are key. Boris Cherny, the creator behind Claude Code, has unveiled a groundbreaking workflow involving custom AI agents that promises to revolutionize how developers work. At the heart of this transformation lies the orchestration of multiple specialized AI agents working simultaneously, transforming coding from a linear process to a real-time strategy-like operation. This article explores the nuances of Cherny’s approach and how it can significantly enhance developer productivity.[1][2][3]
What Boris Cherny’s Claude Code Workflow Teaches About Custom AI Agents
Custom AI agents represent the next step in AI-enabled software development. These agents are designed to handle specific tasks autonomously, freeing up human developers to focus on more complex aspects of coding. Cherny's method involves using several AI subagents that manage different coding tasks, illustrating how multi-agent setups can multiply productivity.[3]
- Why Multi-Agent Setups Matter: The ability to deploy several AI agents simultaneously allows for task parallelization, reducing bottlenecks and increasing throughput.
- How This Shifts Developer Roles: By automating routine tasks, developers can shift their focus to strategic decision-making and creative problem-solving.
How Multi-Agent Orchestration Turns Coding Into a Real-Time Strategy Game
Cherny’s workflow incorporates both terminal-based and web-based AI agents that run in parallel. This orchestration mimics a real-time strategy game, where alerts and commands facilitate communication between agents.[3]
- Running Parallel Agents: By using multiple tabs and system notifications, developers can keep track of various tasks, enabling them to respond quickly when an agent requires input.
- Using Notifications and Teleport Commands: Notifications act as real-time updates for developers, while teleport commands allow seamless switching between different environments.
Choosing the Right Model and Minimizing the Correction Tax
In a counterintuitive move, Cherny opts for the slowest, yet most sophisticated AI model. This strategy minimizes errors and reduces time spent on corrections.[3]
- Why Cherny Prefers the Slowest, Smartest Model: The use of a powerful AI model reduces the need for human correction, ultimately saving time in the development process.
- Compute Tax vs. Correction Tax: This tradeoff highlights the importance of selecting the right AI model to optimize efficiency and accuracy.
Operational Practices: CLAUDE.md, Slash Commands, and Subagents
Cherny’s team uses innovative solutions to ensure AI effectiveness and efficiency.[3]
- Shared-Rule Files to Solve AI Amnesia (CLAUDE.md): This file acts as a memory bank, ensuring AI agents learn from past mistakes and improve over time.
- Slash Commands and Repeatable Shortcuts: By utilizing custom commands, Cherny’s team speeds up repetitive tasks, further enhancing productivity.
- Designing Subagents for Verification and Cleanup: Specialized subagents focus on verifying code and conducting cleanup processes, ensuring high-quality outputs.
Verification Loops: Making Agents Test and Prove Their Work
Verification loops are essential for maintaining quality in AI-generated code.[3]
- Browser Automation, Test Suites, and Self-Verifying Agents: These elements allow AI to independently test and verify its work, ensuring it meets quality standards.
- How Verification Improves Quality and Speeds Delivery: With AI assuming testing roles, human efforts can focus on more valuable tasks, accelerating the delivery pipeline.
Business Impact and Next Steps for Engineering Teams
The business implications of employing multi-agent workflows are profound, offering significant productivity gains and competitive advantages.
- Measuring Productivity Gains: Reports indicate potential productivity improvements of up to 5x, a metric worth exploring for any forward-thinking engineering team.
- How to Pilot Multi-Agent Workflows at Your Company: Initiating pilot programs with custom AI agents can provide firsthand insights into their potential benefits.
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Key Takeaways
Boris Cherny's workflow showcases the transformative power of custom AI agents in coding environments. By leveraging multi-agent orchestration, developers can achieve significant productivity gains, while strategies like verification loops ensure high-quality outcomes. As AI continues to evolve, the balance between human input and machine automation will reshape the future of software development.
Martin Kuvandzhiev
CEO and Founder of Encorp.io with expertise in AI and business transformation