Someone on your revenue operations team got tired of nagging account executives about CRM hygiene. So they wired up an agent. Salesforce has an MCP server, the model can call tools, and the workflow is obvious: take the meeting transcript, pull out the next steps, update the opportunity, log the activity, push a follow-up task. An afternoon of work, one API token in a .env file, and the thing runs.
It works. AEs stop complaining. The demo gets passed around. Within a week, two other teams want the same thing for Zendesk and Jira, and you have quietly become the owner of production Agentic AI inside the company.
Then it stops being an afternoon project. Not because the agent got worse, but because the moment it acts on behalf of other people, every shortcut that made the prototype fast turns into a question you cannot answer with a print() statement.
TL;DR
You need an MCP runtime for your AI Agents when auth, permissions, audit logs, integrations, reuse, or risk ownership start moving out of the prototype phase.
