MCP Dev Summit Seoul 2026

Two years ago, getting an AI agent to reach anything outside itself meant writing a whole bunch of glue code. A custom integration for every tool, every data source, every model you wanted to support. Nobody enjoyed this, and yet everybody did it anyway.

MCP was the emerging alternative - one protocol, implemented once on each side. It empowered all imaginable agents to be able to talk to anything through a standard set of conventions.

Two years later, the ecosystem around MCP is bigger than the spec itself. There are servers for nearly everything anyone would want to wire an agent into, clients across every major platform, and entire categories of capabilities that make the protocol suitable for more than just ferrying text-based data back and forth.

MCP is now the undeniable substrate most agentic workflows run on. Getting there took a couple of years, a large number of engineering debates, and the work of a community of maintainers, developers, and advocates that spans the globe. This is the story of us getting here.

Moving MCP tooling and agents from prototype to production creates a new set of challenges. Some are widely discussed protocol and infrastructure issues: scalability; agent communication; authentication. But as organizations work through those challenges, another bottleneck comes to the front: the operational governance layer around MCP adoption: trust, context, policy, provenance, accountability, and ecosystem participation

That layer is familiar territory for open source management. For years, Open Source Program Offices (OSPOs) practices have coordinated across legal, security, engineering, and product teams, helping decide which open source software to trust, how to contribute safely, how to manage provenance, how to define reusable policies, and more

As MCP makes it easier for agents to connect to tools, data, and internal systems, teams also need to ask: Which MCP servers are safe to use? Who maintains them? What context should agents read? What internal policies should be enforced? How do we trace what an agent did and under what rules?

Building on preliminary findings from the 2026 State of the OSPO survey, early stories from open source managers and strategists across organizations, and emerging agentic AI use cases being tested in the field, this keynote explores how OSPO practices can help organizations adopt MCP in production through ways that are trustworthy, auditable, context-aware, and connected to the AAIF ecosystem.

To attach an MCP server to each of hundreds of Naver Cloud Platform services, every service team had to implement MCP from scratch.
We built an MCP Generator to solve this — automatically generating MCP server code from OpenAPI specs.

Then Claude's MCP Builder Skills arrived. Suddenly, anyone could build an MCP server through a few conversations with Claude. The value of the generator we had built disappeared overnight.

We had to change direction.
And the question changed with it — from "how do we make building MCP servers easier?" to "how do we operate hundreds of them?"

That's how MCP Gateway was born. It aggregates multiple MCP servers behind a single HTTP endpoint, so users can connect every cloud service tool to their AI agent with just one URL and one API key.

This talk is structured in two parts:

Part 1 — The Journey: The process of figuring out how to provide good MCP servers for hundreds of cloud services — what problems we found, what we tried, and how the arrival of MCP Builder Skills became the turning point.

Part 2 — MCP Gateway: An introduction and demo of what problems it solves today, and a look at what we're building next.

Despite growing investment in AI, most enterprises still struggle to convert adoption into measurable business results. This session breaks down the structural root causes behind that execution gap and the operational risks created as AI agents proliferate across the organization. We'll then introduce Enterprise MCP, a governance and control framework built for enterprise-level AI oversight, covering how to design performance measurement systems and take a practical approach to agent-based automation that scales safely.

Connecting AI agents and exchanging data is only the first step toward enterprise adoption. True interoperability requires common approaches for identity, authorization, governance, observability, and communication across heterogeneous systems built by different vendors.
This keynote explores the broader industry challenge of building interoperable agent ecosystems and presents AWS’s experience supporting multi-agent collaboration across both the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication models. The session will discuss architectural patterns and enterprise considerations for enabling secure, scalable, and interoperable AI agents built on open standards, while sharing practical lessons learned from real-world deployments.
Attendees will gain practical insights into the future of interoperable AI agent infrastructure, the role of open standards in accelerating enterprise AI adoption, and the architectural foundations needed to build secure, scalable, and production-ready agent ecosystems.