MCP in Practice: From APIs to Agent Orchestration

Putting MCP into practice means designing tools that are coarse enough to be useful but fine enough to stay safe. We share lessons from integration projects where MCP servers wrap existing APIs and data sources, and agents perform tasks like status checks, report generation, and exception handling.

We also discuss when to use agent-driven flows versus traditional ETL or API pipelines: agents excel at exploratory and decision-heavy steps; pipelines remain better for high-volume, deterministic data movement.

You'll leave with a clearer picture of where MCP and AI agents fit in your integration roadmap.