As organizations experiment with AI-driven automation, a practical question quickly emerges: when do you rely on predictable workflows, and when do you allow agents to act autonomously? In this session, we explore that trade-off using a real-world mail processing case, comparing Autonomous Copilot Agents and Agent Flows in Copilot Studio with Power Automate workflows using AI Builder and prompt-based actions.

Using incoming e-mail as a concrete example, we show how AI can categorize messages, draft replies from enterprise knowledge, and still keep humans in the loop where it matters. We discuss requirements such as speed, auditability, control, exception handling, and trust. Ultimately, it depends on whether you value predictability over autonomy, or autonomy over predictability.

So, when does your scenario call for a deterministic Power Automate flow, and when is it time to let an autonomous Copilot Agent take the lead?