Generating AI Agents Using ChatGPT and Claude

I've been experimenting with AI agents — autonomous systems that can plan, reason, and execute multi-step tasks. The most effective approach I've found involves combining ChatGPT and Claude, leveraging their complementary strengths.

Why Two Models?

ChatGPT excels at creative ideation and rapid iteration, while Claude shines at structured reasoning and longer-context analysis. My typical workflow uses ChatGPT for brainstorming and code scaffolding, then Claude for review, refinement, and edge-case handling.

The Architecture

A simple coordinator agent delegates tasks to specialized sub-agents, each backed by a different model:

class AgentCoordinator:
    def __init__(self):
        self.chatgpt = OpenAI()
        self.claude = anthropic.Anthropic()

    def brainstorm(self, task: str) -> str:
        return self.chatgpt.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": task}]
        )

    def refine(self, draft: str) -> str:
        return self.claude.messages.create(
            model="claude-sonnet-4-20250514",
            messages=[{"role": "user", "content": f"Review: {draft}"}
        )

Prompt Patterns That Work

• Role assignment: Give each agent a specific persona and expertise area.
• Chain-of-thought: Ask agents to reason step-by-step before producing output.
• Self-correction loops: Have Claude review ChatGPT's output and flag issues.
• Structured outputs: Enforce JSON or markdown for reliable parsing.

Key takeaways:

• Start simple — a two-agent coordinator is enough for most tasks.
• Don't over-prompt. Clear instructions are better than complex ones.
• Always keep a human-in-the-loop for critical decisions.
• Cache responses to reduce cost and latency.

The future of AI agents isn't about finding the single best model — it's about orchestrating multiple models to play to their individual strengths.