Model Context Protocol (MCP) - AI Build & Learn #1

Welcome to AI Build & Learn a weekly AI engineering stream where we pick a new topic and learn by building together.

​This event covers Agentic Search with Tavily. Tavily is a search API built for AI agents and LLMs, designed to retrieve relevant web results and structured content quickly for real-time, grounded answers.

​Resources

• ​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​Events Calendar: https://luma.com/ai-builders-and-learners
• ​Slack (Flyte AI Slack): https://slack.flyte.org/
• ​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​In this stream

• ​Agentic Search with Tavily
• ​Hands-on demo
• ​Community Discussion + practical examples

​Community challenge (optional)
​Try spending 30–90 minutes during the week learning or building something related to MCP, then share what you’re working on in Slack.
​
Note on Flyte / Union
​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​Union.ai: https://www.union.ai/
• ​Flyte: https://flyte.org/

​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).