Conference Description
Key Takeaways
- Technical conference dedicated to the Model Context Protocol (MCP) and production-grade agentic AI systems
- Part of a global summit series presented by the Agentic AI Foundation with Linux Foundation support
- Covers agent architecture, orchestration, security, multi-agent integration, and enterprise deployment
- Designed for software engineers, platform teams, solutions architects, and technical leaders
- Features technologies including Netflix Conductor, Agentspan, Goose, Agents.md, and Naver Cloud Platform
Introduction
The MCP Dev Summit Seoul brings together developers and practitioners working on agentic AI systems for an in-depth exploration of the Model Context Protocol and its role in building interoperable AI infrastructure. Presented by the Agentic AI Foundation with support from The Linux Foundation, this technical conference targets engineers and platform teams responsible for designing, deploying, and operating agent-based systems at scale. As organisations increasingly invest in AI capabilities, the challenge of translating that investment into reliable production systems has become a central concern for technical teams across industries.
About This Event
The summit forms part of a global conference series focused specifically on the agentic AI stack. Seoul serves as a strategic location given the city’s established leadership in consumer technology, gaming, and AI platform development. The event combines keynotes, breakout sessions, technical talks, and case studies with a solutions showcase and networking opportunities. Attendees can also pursue professional certification through the MCP Architect (MCPA) programme, reflecting the event’s emphasis on practical skill development alongside conceptual understanding.
The Model Context Protocol and Interoperability Standards
At the centre of the summit’s technical programme is the Model Context Protocol, a foundational standard designed to enable interoperability between agentic AI systems. MCP addresses one of the persistent challenges in enterprise AI adoption: connecting disparate agent implementations so they can communicate, share context, and coordinate actions reliably. For organisations operating complex IT ecosystems, adopting open standards like MCP can reduce integration friction and avoid vendor lock-in while enabling more sophisticated multi-agent architectures.
The protocol’s development reflects broader industry movement toward standardisation in AI infrastructure, similar to how containerisation standards transformed cloud-native computing. Sessions at the summit explore both the protocol’s technical specifications and practical implementation patterns for teams building production systems.
Technical Programme and Discussion Topics
The conference programme spans the full lifecycle of agentic system development. Agent architecture sessions examine design patterns for building robust, maintainable agent implementations, while orchestration tracks address the coordination challenges that emerge when multiple agents must work together on complex tasks. Infrastructure discussions cover the platform requirements for running agentic workloads, including compute, storage, and networking considerations specific to AI systems.
Security receives particular attention, reflecting the heightened risk profile of autonomous systems that can take actions on behalf of users and organisations. Production operations content addresses monitoring, observability, and reliability engineering for agentic deployments. Technologies featured in the programme include Netflix Conductor for workflow orchestration, Agentspan for agent observability, Goose as an open source AI agent, and Agents.md for agent documentation standards. Platform-level discussions reference PyTorch, Naver Cloud Platform, and Google Cloud implementations.
Enterprise Integration and Operational Challenges
A significant portion of the summit addresses the practical realities of deploying agentic systems within enterprise environments. Multi-agent integration presents particular challenges when existing IT infrastructure, security policies, and compliance requirements must be accommodated. Case studies from practitioners who have navigated these challenges provide attendees with concrete implementation guidance rather than theoretical frameworks alone.
The operational dimension extends beyond initial deployment to ongoing management of agentic systems in production. Teams must establish appropriate guardrails, implement effective monitoring, and develop incident response procedures suited to autonomous systems that may behave unpredictably under novel conditions.
Who Should Attend
The summit is structured for technical practitioners rather than executive audiences. Software engineers and backend developers building agent implementations will find detailed architectural guidance, while solutions architects can explore integration patterns for enterprise deployments. Platform teams responsible for AI infrastructure gain exposure to operational best practices and tooling options. Open source maintainers and technical leads benefit from direct engagement with the MCP community and protocol development discussions. The event also serves project managers overseeing agentic AI initiatives who need deeper technical understanding to guide their teams effectively.
Industry Context
The emergence of dedicated conferences for agentic AI reflects the technology’s transition from research curiosity to production reality. Organisations across cloud platforms, enterprise IT, and developer tooling are actively building agent capabilities, creating demand for shared standards and implementation knowledge. The MCP Dev Summit positions itself at this intersection, serving as a technical hub where engineers building large-scale AI services can exchange practical insights and contribute to the protocols that will shape how agentic systems interoperate in the years ahead.