Stop letting your AI agent eyeball A/B picks — wire in a real contextual bandit via MCP (free, no key)

The article highlights the pitfalls of letting AI models blindly choose A/B variants based on highest conversion rates, emphasizing their lack of understanding of sample size, exploration, and regret. Instead, it advocates for using a more sophisticated method called a "contextual bandit" via MCP, which is more effective in continuously optimizing choices over time. This matters because in dynamic environments where A/B testing is part of an ongoing loop, simple heuristics can lead to suboptimal decisions, while more advanced methods can significantly improve outcomes.