MCP Dev Summit Seoul 2026

The Model Context Protocol (MCP) is emerging as a standard for connecting AI agents with external tools. While LLM-based tools are flexible, their non-deterministic behavior makes it difficult to meet requirements for stability, repeatability, and auditability in production systems. In such cases, traditional machine learning models provide more predictable inference and are often preferred for system-critical decisions.

However, ML-powered tools are not free from challenges. Latency, failures, and integration issues can propagate through MCP systems and impact agent behavior, leading to inconsistent decisions, repeated retries, or silent failures.

This session explores what happens when MCP tools fail and how to design systems that remain reliable under such conditions. We present a reproducible demo of failure scenarios, including latency spikes and unstable predictions, and show their impact on agent behavior. We then introduce practical design patterns—such as timeout and retry strategies and observability—to build reliable MCP-based systems.

Attendees will gain concrete techniques to design trustworthy AI systems that remain stable in real-world environments.

As MCP adoption grows, most implementations focus on defining tools and integrating APIs, but far less attention is given to how these tools are actually executed. In production environments often introduces real challenges in both security and performance.

In this session, we explore how WebAssembly fundamentally changes this model by providing a secure and high-performance execution layer. We'll also work through how MCP tools can run inside a sandboxed WebAssembly runtime, where access to the file system, network, and environment is explicitly controlled. This significantly reduces the attack surface compared to traditional server-side execution models, especially in multi-tenant and agent-driven environments.

From a performance perspective, WebAssembly enables near-native execution speed with extremely fast startup times. WebAssembly modules can start in milliseconds, making them ideal for MCP workloads where tools are invoked frequently and latency directly impacts agent responsiveness.
We will also cover authentication patterns such as OAuth and JWT, and explain how a “secret-less” architecture can be achieved by isolating credentials outside of the execution runtime.

Korea's industrial conglomerates—chaebols like Samsung, SK Hynix, Hyundai, LG, and POSCO—operate the most sophisticated manufacturing ecosystems on the planet. Behind the scenes, each utilizes thousands of highly proprietary, heavily siloed Operational Technology (OT) and Information Technology (IT) systems resulting from decades of organic growth and acquisitions. Currently, engineers waste weeks manually extracting, normalizing, and correlating data from these isolated environments before any advanced AI analysis can even begin. This session explores the transformative role of the Model Context Protocol acting as a universal, cloud-native translation layer. We will reveal how manufacturing giants are utilizing Kubernetes-hosted MCP platforms to seamlessly bridge legacy industrial control systems with modern AI agents, radically accelerating industrial intelligence.

Same MCP server. Same prompt. Three different IDEs. Three different outcomes — and one of them is silently wrong.

After documenting a production MCP server across Cursor, Claude Desktop, and Windsurf, the speaker has the war stories. Tool descriptions one client truncates and another expands. Resource limits that vary by 10x. Notification semantics that work in one host and silently no-op in another. OAuth flows that route through different dances depending on the client. Session-lifetime assumptions that turn into "why did my context vanish?" support tickets.

This talk is a field guide, organized by client, with the actual diffs server authors should care about. Live side-by-side traces of the same tool call across three clients, showing where each one quietly diverges from the spec — and the small defensive changes (in tool naming, description length, error semantics) that make a single server work the same way in all three.

Closes with a proposal: a "client compatibility matrix" the MCP ecosystem should publish and maintain, modeled on Can I Use for the web.

For MCP server authors who have only ever tested in one client.

AI agents are increasingly expected to work with visual information: documents, screenshots, cameras, inspection images, and other domain-specific signals. MCP gives developers a powerful way to expose these capabilities as discoverable tools and resources, but real-world computer vision brings challenges that do not disappear behind a simple tool call, including image quality, sensor differences, domain shift, uncertainty, validation, and human review.

In this session, I will explore practical patterns for integrating computer vision into the MCP ecosystem, including OCR, object detection, visual monitoring, and domain-specific inspection tools. The talk will focus on what MCP can standardize for agent-facing vision systems, what should remain domain-specific, and how developers can design safer, more reliable interfaces between AI agents and visual perception.

GPU infrastructure has traditionally been operated through CLIs, dashboards, and a patchwork of admin tools. Each task — checking cluster state, allocating resources, launching sessions — forces the operator to switch context and stitch results together manually.

MCP changes the surface of this work. By exposing infrastructure operations as MCP tools, an agent can carry out cluster tasks on the operator's behalf. And with MCP Apps (SEP-1865), the agent doesn't just respond in text — it renders interactive UIs inline, so the operator can see the cluster, review the agent's plan, and approve actions visually within the same conversation.

This talk shows how an agent embedded in an MCP host can manage GPU infrastructure efficiently by combining:

- **Visual control via MCP Apps** — the agent renders cluster status views, resource allocation charts, and confirmation cards inline. The operator sees the infrastructure, reviews the plan, and clicks to approve.
- **Session-level control via MCP** — the agent creates, inspects, and modifies compute sessions through MCP tool calls.
- **Human-in-the-loop by design** — visual checkpoints make every consequential action explicit