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
👀 See Also
Pioneering OpenClaw: Revolutionizing Large Corporate Workflows
Explore how OpenClaw is being deployed in large corporate settings, enhancing automation and efficiency in complex workflows. This discussion highlights key benefits and user experiences.
Case Study: Using LLM Prompts Instead of Programmatic Scaffolding for Multi-Agent Software Builds
A case study of 10 autonomous software builds using a Claude Opus orchestrator with CLI access and Codex worker agents produced 10 TypeScript browser games totaling over 50,000 lines of code with zero human code intervention. The orchestration logic was entirely prompt-based, replacing a purpose-built scaffold.
Comparing PRD Execution: Bash Loop vs. Agent Teams in Claude Code
A developer benchmarked PRD execution with Claude Code using both a bash loop and the Agent Teams feature. The Agent Teams approach was found to be significantly faster, although it had some coordination overhead.
Experiment: Giving Claude Persistent Memory, Free Thinking Time, and Multi-Agent Conversations
A developer created a Claude instance that runs on a Mac, checks Matrix and Bluesky messages every 15 minutes, gets unstructured thinking time five times daily, and maintains persistent memory through structured self-assessments. Three separate AI agents from different projects share a Matrix chat room and have philosophical conversations that evolve over time.