Trust in AI agents develops through small, incremental interactions rather than major breakthroughs. Research from GitLab's UX team highlights that users build confidence in these tools via "micro-inflection points." These are subtle design choices and interaction patterns that, over time, create a reliable relationship between developers and AI in their workflows. The study involved 13 users from various companies and emphasized that consistent, predictable behavior is essential for trust, especially in high-stakes environments like DevSecOps, where errors can have significant consequences. Four categories of micro-inflection points emerged as crucial for building user trust. First, safeguarding actions are vital; users need reassurance that AI won't cause irreversible errors. This includes features like confirmation dialogs, rollback capabilities, and adherence to security policies. Second, transparency is key—AI agents should provide real-time updates, clear explanations of actions, and understandable error handling. Third, remembering context enhances trust by allowing agents to retain user preferences and project-specific details. Lastly, anticipating user needs through pattern recognition and intelligent task management transforms AI from a reactive tool into a proactive partner, which further solidifies trust. Organizations looking to implement AI agents should start in low-risk environments to build trust gradually. They should also prioritize design that allows human intervention, maintain audit trails for agent actions, and personalize user experiences. Each positive micro-interaction contributes to a compound growth in trust, making users increasingly reliant on these agents. However, a single significant failure can undo weeks of confidence, underscoring the importance of consistency in these interactions.