OpenClaw user struggles with AI agent automation after successful Claude Code pipeline
Claude Code success vs. OpenClaw agent frustration
A user on r/openclaw shared their experience trying to automate image recreation using nanobanana for their marketing agency clients. They achieved a working pipeline with Claude Code in just one hour by talking with the model, providing API keys, and having it launch multiple tests, use multiple tools to extract backgrounds, and refine template prompts through visual analysis of images.
The user then attempted to teach this process to an AI agent within their OpenClaw setup, running on Gemini 3.1 Pro. The agent exhibited several problems:
- Bad reasoning capabilities
- Slow response times
- Incorrect outputs
- Failure to achieve the same results as Claude Code after nearly a day of attempts
The user suspects the model choice might be the issue, specifically mentioning that "using gem3.1 pro thru vertex is the problem." They're considering two potential solutions: giving their Claude Code to the agent so it could perform the task as quickly as they did, or switching to another model entirely.
The case highlights a common challenge in AI automation workflows: successful results with one model don't always transfer smoothly to agent-based implementations, particularly when different underlying models are involved.
📖 Read the full source: r/openclaw
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