I'm evaluating whether to use the Model Context Protocol for our next Robotic Process Automation (RPA) project. We need to bridge the gap between LLM reasoning and legacy desktop applications. Does MCP provide enough control for complex sequences, or is it better suited for simple data retrieval? I've heard some people say it lacks the "workflow logic" found in traditional Project Management scripts. What are your thoughts on agentic execution?
3 answers
In Robotic Process Automation, the struggle has always been the "brittleness" of scripts. MCP moves us toward "semantic automation." Instead of telling a robot to click "Pixel X, Y," you give an AI agent a tool called submit_invoice. The MCP server handles the low-level execution, while the agent handles the decision-making. However, for highly complex Project Management workflows, MCP alone might be too flat. It doesn't inherently encode "if-this-then-that" logic; it just exposes the tools. You still need an orchestration layer or "Agent Skills" to define the standard operating procedures.
If MCP is just a "flat" tool space, how do we handle errors or retries when a tool call fails in the middle of a sequence?
We use it for the "discovery" part, then hand off to a more rigid workflow engine for the actual heavy lifting.
That's a solid hybrid approach, Diana. Using the right tool for the right job is key in these emerging AI architectures.
Justin, that's where the "Host" application comes in. The protocol includes standardized error messages, but the logic for a "retry" or "fallback" typically lives in the agent's prompt or the application wrapping the MCP client. Essentially, the agent receives the error as context and must use its reasoning capabilities to decide whether to try again, use a different tool, or ask the human for help. It’s a more dynamic way to handle failures than traditional hardcoded RPA loops.