MCP Dev Summit Seoul 2026

This talk shares lessons from rebuilding a production LLMOps feedback system after its original pipeline architecture became too difficult to extend and operate in practice. The system was initially designed as a modular, cloud-native pipeline that handled community exercise data end to end—from collection and analysis to feedback generation and publishing. As new capabilities were added, coordinating cross-step dependencies, managing state, and evolving the workflow without breaking existing logic became a growing challenge.
To address this, we restructured the system around MCP tools. Each core function—data ingestion, exercise analysis, recommendation generation, and feedback delivery—was refactored into an independently callable tool that agents can dynamically orchestrate. We will discuss how we handled multi-step coordination without losing state, partial failures and retries across tool boundaries, observability in agent-driven execution, and secure tool interaction in a production environment. Rather than presenting an idealized architecture, this session focuses on what changed in practice: what worked, what didn’t, and what we would do differently.