Multi-agent workflows enable Mule to tackle complex tasks by coordinating specialized AI agents. This advanced feature allows you to decompose tasks into smaller pieces, with each handled by an agent with specific expertise.

How Multi-Agent Workflows Work

In multi-agent workflows:

  1. Each agent is responsible for a specific aspect of the task
  2. Agents execute in a defined sequence
  3. Output from one agent becomes input for the next
  4. The final result integrates all agents’ contributions

This approach enables more complex reasoning and better results than using a single agent for everything.

Types of Specialized Agents

Multi-agent workflows can incorporate various specialized agents:

Analysis Agents

These agents focus on understanding code and requirements:

  • Code analyzer: Examines existing code to understand patterns and structure
  • Requirements analyzer: Breaks down issue descriptions into actionable tasks
  • Test analyzer: Evaluates test coverage and identifies test needs

Design Agents

These agents plan solutions without writing code:

  • Architecture designer: Creates high-level design for complex features
  • Interface designer: Defines API contracts and interfaces
  • Database designer: Plans data structures and relationships

Implementation Agents

These agents write the actual code:

  • Feature implementer: Writes code for new features
  • Bug fixer: Creates patches for specific issues
  • Test writer: Creates comprehensive test suites

Validation Agents

These agents verify quality:

  • Code reviewer: Checks code for best practices and potential issues
  • Security auditor: Identifies security vulnerabilities
  • Performance analyzer: Looks for optimization opportunities

Example Multi-Agent Workflow

Here’s an example workflow for implementing a complex feature:

id: feature-implementation
name: Feature Implementation
description: Implements a new feature with analysis, planning, and testing
steps:
  - id: requirements-analysis
    agentID: 1
    outputField: generatedText
  - id: architecture-design
    agentID: 2
    outputField: generatedText
  - id: implementation
    agentID: 3
    outputField: generatedText
  - id: test-creation
    agentID: 4
    outputField: generatedText
  - id: documentation
    agentID: 5
    outputField: generatedText
validationFunctions:
  - goFmt
  - golangciLint
  - goTest

Input and Output Mapping

One of the key aspects of multi-agent workflows is controlling how information flows between agents:

Standard Mapping

By default, each agent receives the complete output from the previous agent. This works well for sequential reasoning tasks where each agent builds on the previous work.

Custom Field Mapping

For more complex workflows, you can map specific output fields to input fields:

steps:
  - id: code-analyzer
    agentID: 1
    outputField: analysisResult
  - id: implementation
    agentID: 2
    inputMapping:
      codeAnalysis: $.steps.code-analyzer.output
      issueDescription: $.input.issueBody
    outputField: generatedCode

This approach allows:

  • Combining inputs from multiple previous steps
  • Accessing original inputs at any step
  • Filtering specific information for each agent

Special Agent Types

Multi-agent workflows can leverage several special agent types:

Chain-of-Thought Agent

This agent type explicitly shows its reasoning:

<think>
First, I need to understand what the issue is asking for.
The user wants a function to validate email addresses.
I should use a regex pattern for this.
Let me write a function that:
1. Takes an email string as input
2. Returns a boolean indicating validity
3. Uses a proper regex pattern for emails
</think>

func IsValidEmail(email string) bool {
    pattern := `^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$`
    re := regexp.MustCompile(pattern)
    return re.MatchString(email)
}

The <think> section is stripped when using the generatedText output field but preserved with generatedTextWithReasoning.

Tool-Using Agent

This agent has access to external tools like:

  • Code search
  • Documentation lookup
  • Command execution
  • File operations

These tools enhance the agent’s capabilities by providing external context and validation.

Best Practices

For effective multi-agent workflows:

  1. Clear agent responsibilities: Each agent should have a specific, focused task
  2. Thoughtful sequencing: Order agents logically based on dependencies
  3. Appropriate context passing: Only pass relevant information between agents
  4. Specialized prompts: Customize each agent’s prompt for its specific task
  5. Validation between steps: Consider adding validation between critical steps

Example: Bug Fix Workflow

Here’s a real-world example of a multi-agent workflow for fixing bugs:

  1. Bug analyzer: Examines the issue description and related code to understand the bug
  2. Test writer: Creates a failing test that reproduces the bug
  3. Bug fixer: Implements a fix that makes the test pass
  4. Reviewer: Checks the fix for potential side effects and ensures best practices

By distributing the work across specialized agents, the quality and robustness of the solution improves significantly.

Limitations and Considerations

When planning multi-agent workflows, consider:

  • Token usage: More agents means higher token consumption
  • Latency: Sequential processing increases overall completion time
  • Error propagation: Mistakes in early steps can amplify through the chain
  • Complexity management: Balance the benefits of specialization against increased system complexity