Copilot Studio makes it easy to add AI capabilities to your solutions; sometimes even too easy. It’s tempting to solve every problem with another prompt, another agent flow, or by letting an LLM interpret logic at runtime.

This is a “just because you can, doesn’t mean you should” session.

Using a realistic support-agent scenario, we explore how Copilot Studio is meant to be used, and where it starts to bend under the wrong kind of complexity. We compare built-in knowledge, agent flows, custom connectors, REST APIs, and Model Context Protocol (MCP), and show what each option is actually good at.
This will be demo-heavy!

Along the way, we call out common anti-patterns: treating LLMs like scripting engines, hiding business rules in prompts, overloading agent flows with logic, or uploading “all the documents” and hoping for the best. For each case, we discuss better alternatives and clear decision rules for when Copilot Studio is the right place, and when it’s time to extend it with Azure or other services.

The goal is not to show everything Copilot Studio can do, but to help you make better design decisions when building real solutions on top of it.