How to Set Up a Hybrid AI Workflow Using Claude and Codex Together

Mar 12, 2026

I kept switching between AI coding assistants, trying to find the “perfect” one. Claude was great at understanding context but sometimes over-engineered solutions. Codex was fast at generating code but missed the bigger picture. Then it hit me: why not use both?

After months of trial and error, I’ve settled on a hybrid workflow that treats each AI for its strengths. Here’s what I learned.

The Problem with Single-AI Workflows

When I first started using AI coding assistants, I tried to make one tool do everything:

Claude-only approach: Great explanations, but I found myself waiting too long for simple boilerplate code. Sometimes it would overthink simple tasks.
Codex-only approach: Fast code generation, but I spent more time explaining context than I saved. The code worked, but often missed edge cases I hadn’t thought to mention.

The real issue wasn’t the tools—it was my expectation that one AI could be everything.

The Hybrid Approach: Divide and Conquer

The breakthrough came when I started treating Claude as my “senior engineer” and Codex as my “implementation assistant.” Here’s the mental model:

┌─────────────────────────────────────────────────────────┐
│                    HYBRID WORKFLOW                       │
├─────────────────────────────────────────────────────────┤
│                                                          │
│   CLAUDE (Strategy & Quality)                           │
│   ├── Requirements gathering                            │
│   ├── Architecture design                               │
│   ├── Code review                                       │
│   └── Edge case identification                          │
│                                                          │
│   CODEX (Speed & Implementation)                        │
│   ├── Boilerplate generation                            │
│   ├── Test writing                                      │
│   ├── Refactoring execution                             │
│   └── Documentation updates                             │
│                                                          │
└─────────────────────────────────────────────────────────┘

Phase 1: Planning with Claude

I start every feature by having Claude help me think through the problem. Not write code—just think.

What I ask Claude to do:

Requirements gathering: I describe the feature in plain English, and Claude helps me identify edge cases I missed.
Architecture design: I share my proposed approach, and Claude critiques it. This has saved me from at least three major rewrites.
Documentation creation: Before any code, I have Claude generate technical specs. This becomes the “contract” for implementation.

Here’s a typical planning session:

I need to add user authentication to my Flask app. Here's what I'm thinking:

1. Use Flask-Login for session management
2. Store passwords with bcrypt
3. Add rate limiting on login attempts

What edge cases am I missing? What could go wrong?

Claude’s response usually includes things I hadn’t considered:

Session fixation vulnerabilities
Password reset flow edge cases
Concurrent login handling
Token expiration strategies

This planning phase takes 10-15 minutes but saves hours of rework.

Phase 2: Implementation with Codex

With documentation in hand, I switch to Codex for implementation. The key is providing clear context.

What works:

Based on the following spec, implement the login endpoint:

[SPEC FROM CLAUDE]

Requirements:
- POST /auth/login
- Accept JSON: {email, password}
- Return JWT token on success
- Rate limit: 5 attempts per minute per IP

Use the existing User model from models/user.py.

What doesn’t work:

Add login to my Flask app

The difference is specificity. Codex needs context, but it doesn’t need to understand the “why”—that’s Claude’s job.

Phase 3: Review with Claude

After Codex generates code, I send it back to Claude for review. This is where the hybrid approach really shines.

My review checklist for Claude:

Security review: “Check this code for security vulnerabilities”
Edge case validation: “What happens if the database is unavailable?”
Best practices: “Is this idiomatic Python/JavaScript/etc.?”

Claude catches things Codex misses:

Missing error handling
Potential race conditions
Inconsistent naming conventions
Performance bottlenecks

Phase 4: Iterate

The cycle continues until quality thresholds are met:

Claude plans → Codex implements → Claude reviews → Fix issues → Repeat

I’ve found that 2-3 iterations usually produce production-ready code.

A Practical Example: Building a Rate Limiter

Let me walk through a real feature I built using this workflow.

Step 1: Claude Planning

I described the requirement: “Add rate limiting to my API endpoints.”

Claude helped me identify:

Different rate limits for authenticated vs. anonymous users
Sliding window vs. fixed window trade-offs
Redis-based storage for distributed systems
Graceful degradation when Redis is unavailable

Step 2: Codex Implementation

I gave Codex the spec:

"""
Rate Limiter Specification
==========================

Requirements:
- Limit: 100 requests/minute for authenticated users
- Limit: 20 requests/minute for anonymous users
- Storage: Redis with fallback to in-memory
- Headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset
- Response: 429 Too Many Requests with Retry-After header
"""

Codex generated the implementation in about 30 seconds.

Step 3: Claude Review

Claude found three issues:

Missing Redis connection error handling
Race condition in the sliding window calculation
No cleanup for expired rate limit keys

Step 4: Fix and Iterate

I had Codex fix each issue, then Claude reviewed again. Two iterations later, the code was solid.

Tools I Use to Make This Work

CLI Integration

I created a simple bash script to switch between AIs:

#!/bin/bash

# Claude for planning/review
claude_plan() {
    claude --model claude-3-5-sonnet "Help me plan: $1"
}

claude_review() {
    claude --model claude-3-5-sonnet "Review this code for issues: $1"
}

# Codex for implementation
codex_implement() {
    codex --model gpt-4 "Implement based on this spec: $1"
}

# Main workflow
case "$1" in
    plan) claude_plan "$2" ;;
    implement) codex_implement "$2" ;;
    review) claude_review "$2" ;;
    *) echo "Usage: $0 {plan|implement|review} 'prompt'" ;;
esac

Python Workflow Class

For more complex projects, I use a Python class:

from dataclasses import dataclass
from typing import Optional
import subprocess

@dataclass
class Task:
    description: str
    spec: Optional[str] = None
    implementation: Optional[str] = None
    review_notes: list[str] = None

class HybridAIWorkflow:
    def __init__(self):
        self.tasks: list[Task] = []

    def plan(self, description: str) -> Task:
        """Use Claude for planning"""
        spec = self._call_claude(f"Plan this feature: {description}")
        task = Task(description=description, spec=spec, review_notes=[])
        self.tasks.append(task)
        return task

    def implement(self, task: Task) -> str:
        """Use Codex for implementation"""
        if not task.spec:
            raise ValueError("Task must be planned first")
        implementation = self._call_codex(f"Implement: {task.spec}")
        task.implementation = implementation
        return implementation

    def review(self, task: Task) -> list[str]:
        """Use Claude for review"""
        if not task.implementation:
            raise ValueError("Task must be implemented first")
        review = self._call_claude(f"Review this code: {task.implementation}")
        task.review_notes = self._parse_review(review)
        return task.review_notes

    def _call_claude(self, prompt: str) -> str:
        # Integration with Claude API
        pass

    def _call_codex(self, prompt: str) -> str:
        # Integration with Codex API
        pass

    def _parse_review(self, review: str) -> list[str]:
        # Parse review into actionable items
        pass

What I Got Wrong (So You Don’t Have To)

Mistake 1: Using Claude for Everything

Initially, I had Claude write all the code. The result? Over-engineered solutions that took forever to generate. Claude once wrote a 500-line “simple” config loader.

Fix: Reserve Claude for tasks where quality matters more than speed.

Mistake 2: Skipping the Review Phase

I got lazy and started shipping Codex-generated code directly. This led to a production incident where a missing null check caused cascading failures.

Fix: Never skip Claude review. Even for “simple” changes.

Mistake 3: Not Documenting the Workflow

When I onboarded a new team member, they were confused about when to use which AI. We wasted time re-explaining the process.

Fix: I created a simple decision tree:

Need to think? → Claude
Need to code? → Codex
Need both? → Claude first, then Codex

Cost Considerations

This hybrid approach isn’t free, but it’s cheaper than you might think:

Task	AI Used	Approximate Cost
Planning (5 min)	Claude Sonnet	~$0.10
Implementation	Codex	~$0.05
Review (2 min)	Claude Sonnet	~$0.05
Total per feature		~$0.20

Compare this to the cost of a bug in production or hours of rework.

When This Workflow Doesn’t Work

This approach isn’t a silver bullet. It struggles with:

Rapid prototyping: Sometimes you just need to iterate fast. Skip the planning phase.
Simple fixes: Typos, minor refactors—just use whichever AI is faster.
Highly specialized domains: If your codebase is unique, both AIs will struggle without extensive context.

The Bottom Line

A hybrid Claude-Codex workflow succeeds by leveraging each AI’s strengths:

Claude: Planning, architecture, review, quality-critical code
Codex: Implementation, boilerplate, speed-focused tasks

Start small. Pick one feature and try the four-phase workflow. Once you see the quality improvement, you’ll wonder how you ever worked with just one AI.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!