Large Language Models are powerful, but they are often limited by "stale" knowledge. To move toward true Agentic Coding—where AI plans, executes, and iterates autonomously—we must provide them with real-time, high-fidelity context. The Model Context Protocol (MCP) provides the missing link. This session explores the technical "Deep-Dive" into MCP's primitives, including dynamic prompts and resource interpolation. We'll demonstrate how agents can now query GitHub issues, run tests, and fetch live documentation using natural language. Stop building isolated chatbots and start building integrated AI partners.
Harshavardhan Bajoria
UnStop
Beyond the Prompt: Building Truly Autonomous AI Agents with MCP
Large Language Models are powerful, but they are often limited by "stale" knowledge. To move toward true Agentic Coding—where AI plans, executes, and iterates autonomously—we must provide them with real-time, high-fidelity context. The Model Context Protocol (MCP) provides the missing link. This session explores the technical "Deep-Dive" into MCP's primitives, including dynamic prompts and resource interpolation. We'll demonstrate how agents can now query GitHub issues, run tests, and fetch live documentation using natural language. Stop building isolated chatbots and start building integrated AI partners.