The Agent Finds the Workflow. Your System Runs It. AI agents are good at the part of a workflow that is still unclear. You have a stack of supplier documents and you do not know which fields matter yet. You have product images and a catalog PDF, but the final listing format is still changing. You have client research PDFs and need to discover which evidence belongs in the final brief. In those moments, writing production code first is premature. The workflow is not known yet. That is where MCP fits. Instead of writing throwaway scripts to answer those questions, you can give an agent real tools and let it explore the workflow directly. The agent can inspect files, try extraction schemas, convert documents to Markdown, generate sample reports, create spreadsheets, and show you what works before you commit to code. That does not mean the agent should own the workflow forever. Once the workflow is known, the stable path should move into a controlled automation platform, REST , or an SDK .…