A multi-agent AI workflow is a system where multiple specialized AI agents collaborate to achieve a complex goal that would be difficult for a single, generalist AI to handle. Instead of relying on one AI to understand architecture, write code, and validate tests, you create a team of experts. Each agent has a distinct role, a specific set of tools, and a clear objective, all coordinated by an orchestrator to work like a human software team.

Orchestrating an AI team involves defining roles, establishing communication channels, and managing the project’s state. The process mirrors a traditional agile team, with each agent performing a specialized function in a sequence. Frameworks like Microsoft’s Autogen or CrewAI provide the structure to define these agents and manage their interactions.

A typical workflow for a large-scale refactoring project might look like this:

1. The Orchestrator (or Project Manager): This initial agent receives the high-level goal, such as “Refactor the monolithic billing service into microservices.” It then breaks down the task and assigns it to the appropriate specialist agent.
2. The Architect Agent: This agent’s job is to analyze the existing codebase. It uses tools to read files, trace dependencies, and understand the current architecture. Its output is a detailed refactoring plan, including API contracts for the new microservices and a sequence for migration.
3. The Code Migration Agent: This agent receives the architect’s plan and begins the hands-on work. It reads specific files, applies the planned changes, and writes the new, refactored code. It operates in small, iterative steps, focusing purely on implementation.
4. The Test Validator Agent: After the migration agent completes a task, this agent takes over. It can either update existing unit tests or generate new ones to validate the refactored code. It runs the test suite, analyzes the results, and reports back to the orchestrator.

This entire process is cyclical. The orchestrator routes reports of test failures back to the code migration agent for debugging, and the cycle continues until the tests pass and the task is complete. The agents pass information and artifacts (like code files or test results) to each other, maintaining a shared understanding of the project’s state.

Using a team of specialized AI agents for complex refactoring offers several advantages over using a single AI. This approach divides a massive cognitive load into manageable, focused tasks, leading to better and more reliable outcomes.

Key benefits include:

• Deeper Specialization: An agent focused solely on static analysis can be prompted and tooled to find architectural patterns more effectively than a generalist agent trying to do everything at once.
• Improved Fault Isolation: When a monolithic AI fails, debugging is difficult. In a multi-agent system, if the test validator fails, you know exactly which part of the workflow to investigate.
• Scalability and Parallelization: You can run multiple instances of code migration agents in parallel to work on different parts of the codebase simultaneously, speeding up large projects.
• More Auditable Processes: The structured, step-by-step nature of the workflow creates a clear audit trail. You can review the plan from the architect, the code changes from the migrator, and the results from the validator at each stage.

While powerful, orchestrating AI agents is not a solved problem. The process comes with unique technical and practical challenges that require careful consideration and engineering.

• Maintaining Context: LLMs have finite context windows. Passing the entire state of a large codebase to each agent is impractical. Successful workflows require intelligent systems for summarizing context and providing agents with only the information they need for their specific task.
• Ensuring Determinism: The creative nature of LLMs can be a drawback when you need predictable results. Getting an agent to produce the exact same output for the same input consistently is a major challenge.
• Tooling and Environment Access: Agents need a secure and reliable way to interact with a real development environment. They need to read and write files, run shell commands, and access network resources, which introduces significant security and operational complexity.
• Defining “Done”: An AI agent might think its job is done when the code compiles or a few tests pass. Defining a robust, comprehensive definition of “done” that covers edge cases, performance, and security is critical for ensuring the final output is production-ready.

Building a successful multi-agent workflow is an iterative process. Start with a human-centric approach and gradually increase autonomy as you build trust in the system.

• Start with a Human in the Loop: Don’t aim for full automation on day one. Initially, have your agents propose changes or plans that a human developer reviews and approves. This lets you validate the agents’ reasoning before giving them the keys to the codebase.
• Define Clear Roles and Explicit Goals: Each agent should have a “prompt” that clearly defines its persona, responsibilities, constraints, and the exact format of its expected output. A well-defined role reduces ambiguity and improves performance.
• Provide Agents with High-Quality Tools: Agents are most effective when they can execute functions to get information or perform actions. Equip them with a library of reliable, well-documented tools, such as functions to run the test suite (run_tests()) or lint a file (lint_file(path)).
• Establish a Clear Communication Protocol: Determine how agents will hand off work. This could be a simple sequence, where the output of one agent becomes the input for the next, or a more complex system where a central orchestrator directs traffic based on results.

As AI agents become more autonomous, they transition from being simple script runners to becoming non-human identities operating within your systems. These agents need to interact with code repositories, APIs, and other services, and they must do so securely. This is where robust authentication and authorization become critical.

You can’t have an AI agent operating with a shared, overly-permissive API key. Each agent should have its own identity with narrowly scoped permissions. Kinde’s machine-to-machine (M2M) authentication is designed for this exact scenario.

By registering each AI agent as a separate M2M application in Kinde, you can issue it a unique client ID and secret. The agent can then use these credentials to obtain a secure OAuth 2.0 access token. This token grants the agent specific, auditable permissions—for example, read-only access to a code analysis API or write access to a specific testing database. This ensures that even if an agent is compromised or behaves unexpectedly, the potential damage is limited to its tightly controlled set of permissions. This practice provides the security and auditability needed to deploy AI workflows confidently in a production environment.

• Set up Kinde Management API access
• Call the Kinde Management API