What is a multi-agent orchestrator design pattern in AI coding?

A multi-agent orchestrator design pattern is an approach where multiple specialized AI agents are assigned different tasks within a single workflow. Instead of relying on one agent to handle everything in a single context, the work is split among dedicated agents (e.g., frontend agent, backend agent, security validation agent). This prevents context overload and allows each agent to focus on its area of expertise, resulting in more reliable and higher-quality output.

Why should I create a custom planning command instead of using the default planning mode in AI coding tools?

Creating a custom planning command gives you greater control and consistency. It allows you to enforce company-wide development methodologies, tailor the plan format to your project's complexity level (e.g., a simple task list vs. full PRD or UML diagrams), and optimize the process through prompt engineering techniques like multi-agent systems. This ensures that every developer on your team follows the same structured workflow, leading to more predictable and maintainable results.

How does plan validation with multiple AI agents improve AI-generated code quality?

Plan validation uses separate AI agents, each running in its own context, to review the generated plan from different angles. One agent checks for clean code and architectural issues, another scans for security vulnerabilities, and a third adds end-to-end testing tasks (e.g., using Playwright) to verify the changes actually work. This "LLM as a judge" approach catches problems early at the planning stage, making the difference between a basic plan and a robust, reliable one that leads to fewer bugs and rework during implementation.

Vibki’s Workflow

After we understand the fundamentals of LLMs and AI, we can start doing the fun stuff. In this blog post, you will see an example of custom planning using the multi-agent orchestrator design pattern. To understand what I'm talking about, I've added this to my open-source repo here: link to the command.

Why do we need a custom plan?

Before we continue, let’s understand why you might need a custom command or skill plan. You can always use the default planning mode of your favourite AI coding tool, but there are times when creating a custom plan is much better:

Company Methodologies: I highly recommend creating your own commands and skills to ensure a consistent development process across all developers in the company.
Specific Plan Formats: Some projects have high technical or product complexity, requiring a PRD (Product Requirements Document) or UML diagrams. In Vibki's case, for example, it's the opposite, most of the technical objectives are straightforward, so there's no need to overload the context with UML diagrams or PRDs. It just needs to be a linear plan (mostly a task list).
Context / Prompt Engineering: When you create your own custom planning command, you can optimize the process by using multi-agent systems for better context engineering (or any other way).

Explanation of create-plan.md

Let’s look at a high-level overview of what this command does.

Goal: Understand the user's request and create a linear task list assigned to specific agents that will execute the tasks (in different contexts). It uses multiple agents to verify the plan and ensure we are solving the right issue.

steps:

Ask What the User Wants to Build: Simply ask what feature we are going to work on.
Verify Understanding: The AI will make sure it understands what it needs to do and which files to touch, and will ask the user clarification questions if needed.
Analyze and Determine Agents: In this step, the system figures out the process and decides which agents need to be assigned.

💡 Note: I have two main agents that write code—frontend and supabase-local-dev (backend). You can assign whichever agents you use in your own workflow.

Create and Save the Plan: It generates a plan with a specific structure and saves it in a designated folder so I can review it and reference it later if needed.
Validate the Plan: Three agents run in separate contexts (to avoid overloading a single context window). One verifies clean code and architectural issues. Another checks for security vulnerabilities. The last one adds Playwright MCP tasks to verify that the changes actually work. Each agent updates the plan file if necessary.

💡 Attention! I think this is the most important step (Step 6 in the .md file). This validation process (using the LLM as a judge) makes all the difference between a regular plan and a robust, reliable one.

Conclusion

Here we saw a planning process, which is one of the most critical parts of the overall workflow. We explored how to implement an AI coding design pattern like the multi-agent orchestrator, and we saw how adding a validation step can help us achieve much better results.

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Vibki

Vibki’s Workflow - Planning

Why do we need a custom plan?

Explanation of create-plan.md

Conclusion

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