The first-ever MCP Dev Summit brings together 1,000+ developers, architects, and AI practitioners to advance the Model Context Protocol standard for interoperable AI agents. Organized by the Linux Foundation's Agentic AI Foundation (AAIF), this two-day technical conference features hands-on workshops, real-world implementation case studies, and deep dives into MCP architecture, security, and production deployment patterns. Sessions cover MCP integration with enterprise systems (OAuth, Jupyter, Docker), autonomous agent orchestration, real-time human-AI collaboration, and building secure, scalable agentic AI systems. Key topics include dynamic MCP workloads, self-healing agent loops, MCP as a product, and production lessons from early adopters at JP Morgan Chase, HashiCorp, IBM, Okta, Docker, Project Jupyter, and Supabase. Whether you're building MCP servers, integrating AI agents into existing workflows, or designing multi-agent systems, this summit provides the technical depth and vendor-neutral perspective needed to navigate the emerging agentic AI ecosystem.

Date

April 2–3, 2026

Location

TBD (Midtown Manhattan), New York City, New York, United States

Format

In-Person

Event Type

Summit

Status

Completed

Expected Attendance

1000+

Pricing

Paid

Organizer

Linux Foundation (Agentic AI Foundation)

Source

Manual

Discovered

March 28, 2026

Last Updated

2026-03-29

• ✓Software Engineers
• ✓AI/ML Engineers
• ✓Platform Architects
• ✓Developer Relations
• ✓AI Product Managers
• ✓Technical Leaders

For enterprise technical leaders, this summit provides vendor-neutral technical depth on the Model Context Protocol standard that's becoming the interoperability layer for AI agents. Key enterprise value: **Production Implementation Patterns:** Learn from early adopters at JP Morgan Chase, Docker, HashiCorp, IBM, and Okta who are deploying MCP in regulated, high-scale environments. **Standards Alignment:** MCP is being contributed to the Linux Foundation's Agentic AI Foundation (AAIF), signaling industry commitment to open standards over proprietary agent frameworks. Understanding MCP architecture now prevents vendor lock-in later. **Security & Governance:** Sessions on OAuth integration, secure agent orchestration, and safe/secure agentic AI directly address enterprise risk concerns that block AI agent adoption. **Multi-Vendor Strategy:** MCP enables "write once, run anywhere" for AI agents across Anthropic, OpenAI, Google, and open-source models. This reduces switching costs and increases negotiating leverage with AI vendors. **Developer Productivity:** Real-world sessions on Jupyter integration, workflow engines, and autonomous agent loops translate to concrete productivity gains for engineering teams.