Open Source Summit + Embedded Linux Conference North America 2026

Planning a trip is a deceptively complex problem for AI, especially when the journey starts from visual context rather than text. In this session, we present a multimodal-first, local-first agentic architecture where a user uploads an image (e.g. “where is this place?”), and the system builds a travel plan from that visual input using Model Context Protocol (MCP), A2A (Agent-to-Agent), and BeeAI — all running fully locally without cloud dependencies.

The system employs a router and specialist agent pattern, where dedicated agents handle image understanding, hotel search, and flight search, each backed by MCP servers. A multimodal model extracts meaning from the image, after which the router decomposes the task and delegates work through A2A to the appropriate specialists.

We will walk-through how BeeAI manages agent lifecycles, how A2A enables explicit agent collaboration, and how MCP acts as a stable contract layer between reasoning and real-world capabilities. The focus is on practical architecture, configuration, and lessons learned, showing how to build MCP-centric, multimodal systems that remain extensible, reproducible, and maintainable as new agents and tools are added.