Autonomy or Control? Designing AI workflows that Works

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. So together with my colleague Richard Wieringa, we will walk through the decision framework for choosing between autonomy and predictability in AI-driven automation.

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?

Our Three takeaways

• Understand how to choose between Autonomoy and Predicitability based on your use case
• See a real-world demo to help decide how to process e-mails with AI-driven solutions
• You will leave with a clear decision framework for AI-Driven automation