Multi-Agent Orchestration: Running Claude Codex and Gemini Together

A comprehensive guide to orchestrating multiple AI agents (Claude Codex and Gemini) for complex development workflows

Multi-Agent Orchestration: Running Claude Codex and Gemini Together on ZenFlow

This guide demonstrates how to orchestrate multiple AI agents—specifically Claude Codex and Gemini—within ZenFlow to leverage their unique strengths for complex development workflows.

When to Use This Guide

Use multi-agent orchestration when you need to:

• Combine Claude’s superior reasoning and planning with Gemini’s fast code generation
• Handle complex projects requiring different AI specializations (architecture planning vs implementation)
• Create collaborative workflows where agents hand off tasks based on their strengths
• Optimize development speed by routing tasks to the most suitable AI model

Prerequisites

Before starting, ensure you have:

• ZenFlow desktop application running with multi-model access
• Active subscriptions for both Claude Codex and Gemini models
• A project repository configured with ZenFlow
• Basic understanding of ZenFlow’s agent system

Understanding Agent Strengths

Claude Codex Advantages

• Complex Reasoning: Excels at architectural decisions and problem decomposition
• Code Quality: Produces well-structured, maintainable code with excellent documentation
• Context Understanding: Superior at understanding large codebases and business requirements
• Best Practices: Follows industry standards and security practices more consistently

Gemini Advantages

• Speed: Faster response times for routine coding tasks
• Multimodal: Can process images, diagrams, and visual specifications
• Code Generation: Efficient at generating boilerplate and repetitive code patterns
• Language Versatility: Strong performance across multiple programming languages

Setup Steps

1. Configure Multiple Models in ZenFlow

Open ZenFlow Settings and ensure both models are configured:

# Verify model access in ZenFlow
Settings → Models → Available Models
✓ Claude 3.5 Sonnet (Codex)
✓ Gemini 1.5 Pro
✓ Gemini 1.5 Ultra (if subscribed)

2. Create a Multi-Agent Workflow

Create a custom workflow that defines when to use each agent:

# Multi-Agent Development Workflow

## Configuration
- **Artifacts Path**: {@artifacts_path} → `.zenflow/tasks/{task_id}`

## Workflow Steps

### [ ] Step: Architecture Planning (Claude)
Claude analyzes requirements and creates high-level architecture.
Output: `{@artifacts_path}/architecture.md`

### [ ] Step: Implementation Planning (Claude)  
Claude breaks down architecture into implementable tasks.
Output: `{@artifacts_path}/implementation-plan.md`

### [ ] Step: Code Generation (Gemini)
Gemini implements planned components rapidly.
Output: Code files and `{@artifacts_path}/implementation-log.md`

### [ ] Step: Code Review & Optimization (Claude)
Claude reviews Gemini's output for quality and optimization.
Output: `{@artifacts_path}/review-notes.md`

### [ ] Step: Testing & Documentation (Collaborative)
Both agents contribute to testing and documentation.
Output: Test files and updated documentation

Save this as .zenflow/workflows/multi-agent.md in your repository.

3. Agent Selection Strategy

Define clear criteria for choosing agents:

Task Type	Primary Agent	Secondary Agent	Reason
Architecture Design	Claude	-	Superior reasoning and planning
API Design	Claude	-	Better at interface design
Implementation	Gemini	Claude (review)	Faster code generation
Debugging	Claude	Gemini (specific fixes)	Better error analysis
Documentation	Claude	-	More thorough explanations
Boilerplate Code	Gemini	-	Faster repetitive tasks
Image Processing	Gemini	Claude (logic)	Multimodal capabilities

Implementation Examples

Example 1: Building a REST API

Step 1: Architecture (Claude)

Agent: Claude 3.5 Sonnet
Task: Design REST API architecture for user management system

Input: Requirements document
Output: API specification, database schema, security considerations

Step 2: Implementation (Gemini)

Agent: Gemini 1.5 Pro  
Task: Implement API endpoints based on Claude's architecture

Input: Architecture specification from Step 1
Output: Controller files, route definitions, middleware

Step 3: Review (Claude)

Agent: Claude 3.5 Sonnet
Task: Review Gemini's implementation for best practices

Input: Generated code from Step 2  
Output: Optimization suggestions, security improvements

Example 2: Frontend Component Development

Workflow Sequence:

1. Claude: Analyze design requirements and component structure
2. Gemini: Generate React/Vue components rapidly
3. Claude: Review for accessibility, performance, and maintainability
4. Gemini: Implement suggested optimizations
5. Both: Collaborative testing and documentation

Example 3: Database Migration

Parallel Processing:

• Claude: Design migration strategy and rollback procedures
• Gemini: Generate migration scripts and seed data
• Claude: Review migration safety and data integrity
• Gemini: Create automated tests for migration

Advanced Orchestration Patterns

1. Sequential Handoff Pattern

Use Case: Complex feature development requiring careful planning and rapid implementation.

2. Parallel Collaboration Pattern

Use Case: Full-stack development where backend logic and frontend can be developed simultaneously.

3. Specialist Routing Pattern

Use Case: Automatically routing tasks to the most suitable agent based on task characteristics.

Practical Implementation

Setting Up Agent Handoffs

1. Create Task Templates

   ## Task: API Implementation
   
   **Phase 1 (Claude)**: Architecture Review
   - Review requirements: {requirements_file}
   - Output architecture decisions to: `architecture.md`
   - Tag for Gemini handoff: `@gemini-implement`
   
   **Phase 2 (Gemini)**: Implementation  
   - Input: `architecture.md`
   - Generate code following architectural decisions
   - Output progress to: `implementation.md`
   - Tag for Claude review: `@claude-review`
   

2. Use ZenFlow Task Chaining

   # Create sequential tasks
   zenflow create-task --type="architecture" --agent="claude"
   zenflow create-task --type="implementation" --agent="gemini" --depends-on="prev-task"
   zenflow create-task --type="review" --agent="claude" --depends-on="prev-task"
   

Communication Between Agents

Structured Handoff Documents:

# Agent Handoff: Architecture → Implementation

## From: Claude 3.5 Sonnet
## To: Gemini 1.5 Pro
## Task: User Authentication System

### Architecture Decisions
- JWT-based authentication
- Refresh token rotation
- Role-based access control (RBAC)

### Implementation Requirements
1. Create `AuthController` with login/logout/refresh endpoints
2. Implement `JWTMiddleware` for token validation
3. Create `User` and `Role` models with relationships
4. Add password hashing with bcrypt

### Code Patterns to Follow
- Use dependency injection for services
- Follow repository pattern for data access
- Implement proper error handling with custom exceptions

### Success Criteria
- [ ] All endpoints properly secured
- [ ] Tests achieve >90% coverage  
- [ ] Performance benchmarks met
- [ ] Security best practices followed

**Next Agent**: Please implement according to these specifications and report progress in `implementation-progress.md`

Monitoring and Optimization

Performance Metrics

Track the effectiveness of your multi-agent setup:

// Agent Performance Tracking
const agentMetrics = {
  claude: {
    averageResponseTime: "45s",
    taskAccuracy: "95%", 
    bestFor: ["architecture", "debugging", "review"]
  },
  gemini: {
    averageResponseTime: "12s",
    taskAccuracy: "88%",
    bestFor: ["implementation", "boilerplate", "ui-generation"]
  }
};

Quality Assurance Checks

Implement automated checks between agent handoffs:

# .zenflow/quality-gates.yml
handoff_checks:
  claude_to_gemini:
    - architecture_document_exists
    - requirements_clarity_score > 8
    - implementation_tasks_defined
    
  gemini_to_claude:  
    - code_compiles_successfully
    - basic_tests_pass
    - code_coverage > 70%

Troubleshooting

Common Issues and Solutions

Problem: Agents producing conflicting implementations
Solution: Create clearer handoff specifications and use structured templates for communication between agents.

Problem: Context loss between agent switches
Solution: Maintain comprehensive artifact files and use ZenFlow’s task dependency system to preserve context.

Problem: Inefficient agent selection
Solution: Create decision trees and use ZenFlow’s custom workflow triggers to automatically route tasks.

Problem: Quality inconsistency between agents
Solution: Implement mandatory review steps and quality gates between agent handoffs.

Problem: Task dependencies not respected
Solution: Use ZenFlow’s task orchestration features and explicit dependency declarations.

Debugging Multi-Agent Workflows

# View agent task history
zenflow logs --agent=claude --task-type=architecture
zenflow logs --agent=gemini --task-type=implementation

# Check handoff artifacts
ls .zenflow/tasks/{task-id}/handoffs/

# Validate workflow configuration
zenflow validate-workflow multi-agent.md

Best Practices

1. Clear Responsibilities

• Define specific roles for each agent based on their strengths
• Avoid overlapping responsibilities that could cause conflicts
• Create explicit handoff protocols

2. Structured Communication

• Use standardized templates for agent-to-agent communication
• Maintain comprehensive artifact documentation
• Implement quality gates between handoffs

3. Iterative Refinement

• Monitor agent performance and adjust workflows accordingly
• Gather feedback from both agents on task difficulty and clarity
• Continuously optimize agent selection criteria

4. Quality Assurance

• Implement mandatory review cycles
• Use automated testing between agent handoffs
• Maintain code quality standards regardless of implementing agent

Next Steps

Once you’ve mastered basic multi-agent orchestration:

• Scale to Team Workflows: Extend multi-agent patterns to entire development teams
• Custom Agent Personalities: Configure agent personas for specific project requirements
• Automated Orchestration: Implement triggers that automatically select optimal agents
• Performance Optimization: Fine-tune agent selection based on historical performance data

For advanced orchestration patterns, refer to the ZenFlow Orchestrating Agents documentation.

Verification

Your multi-agent orchestration is working correctly when:

• Tasks are automatically routed to appropriate agents based on defined criteria
• Handoffs between agents preserve context and requirements
• Both agents contribute their strengths to the overall development workflow
• Quality gates prevent suboptimal code from progressing through the pipeline
• Overall development speed increases compared to single-agent workflows

By following this guide, you’ll be able to leverage the complementary strengths of Claude Codex and Gemini to create more efficient and higher-quality development workflows in ZenFlow.