MCP Dev Summit Bengaluru 2026

Building an AI sales assistant means pulling data from everywhere , Salesforce for accounts, Snowflake for product catalogs, and internal KBs for pricing. We started by hardcoding each integration into the agent. It worked until every new data source meant rewriting agent code, duplicating auth logic, and redeploying everything. Tool definitions were tightly coupled, making it impossible to evolve the data layer independently.MCP changed everything. In this talk, I'll walk through how we built a production AI sales assistant using LangGraph connected to multiple MCP servers ,each wrapping a different backend, owned by a different team. I'll cover: why direct integrations become unmaintainable at 3+ sources; our reusable FastMCP server template that spins up domain-specific servers in days; how a user's SSO token flows end-to-end from agent through MCP to Snowflake OAuth with zero credential storage; why tool descriptions and agent prompts matter more than the protocol itself , and the patterns we developed (normalize-before-search, dynamic prompt injection) to make the LLM reliably pick the right tool; and what broke in production with real failures and real fixes.