- Updated: March 22, 2026
- 1 min read
Building an Automated CI/CD Pipeline with OpenClaw Agent Evaluation Framework
Developers are riding the wave of self‑hosted AI agents and the recent hype around Moltbook. To keep your customer‑support agent ahead of the curve, you need an automated CI/CD pipeline that continuously ingests evaluation data from the OpenClaw Agent Evaluation Framework.
This step‑by‑step guide shows you how to set up the pipeline, integrate OpenClaw metrics, and automatically deploy improvements to your support bot. You’ll learn how to:
- Configure a Git repository to store your agent code and OpenClaw evaluation results.
- Set up GitHub Actions (or your CI tool of choice) to run tests, linting, and OpenClaw data processing on every push.
- Use the processed metrics to trigger model retraining and configuration updates.
- Deploy the updated agent to your production environment with zero‑downtime.
By automating these steps, your support agent continuously learns from real‑world interactions, delivering better responses over time.
Ready to get started? Follow our detailed walkthrough and watch your AI agent evolve.
For more information on hosting OpenClaw, see our guide.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.