The rise of the agentic economy on the shoulders of MCP — Jan Curn, Apify

This talk introduces GitHub Copilot's Agent Mode, an advanced feature designed for completing moderately complex tasks autonomously. It moves beyond simple code completion and chat interactions to enable deep, iterative engagement with an AI agent. The presentation highlights how Agent Mode can build entire applications from a readme file or perform significant refactoring, with the developer providing permission for actions like terminal interactions.

This talk explores what it means for Model Communication Protocol (MCP) to be enterprise-ready, moving beyond basic tool-use capabilities to address the complexities of production environments. It highlights the evolution from simple chatbot interactions to sophisticated AI agents that require robust security, management, and scalability, particularly when integrating with internal enterprise systems. The core thesis is that while MCP offers a standardized way for AI to interact with external resources, achieving enterprise readiness involves overcoming significant challenges in authentication, authorization, and operational management.

The talk argues that the concept of "data readiness" for AI is a myth, as data is rarely perfect and constantly changing. Instead of striving for pristine data, the focus should be on building AI systems that can reliably work with messy, evolving data. This is achieved by creating an agentic semantic layer that learns and adapts to the specific business domain and its nuances over time, much like an experienced human analyst.

This talk explores the automation of business processes using Large Language Models (LLMs) through two key protocols: A2A (Agent-to-Agent) and MCP (Model Context Protocol). A2A facilitates communication between remote agents, enabling specialization and parallel processing, while MCP acts as a standardized interface for agents to access context and tools, akin to a USB-C for AI. The workshop demonstrates how to integrate these protocols to build multi-agent systems triggered by webhooks, automating tasks like bug reporting and information dissemination.

This talk explores the concept of agentic enterprise, positing that AI agents could fundamentally reshape organizations within three years. It defines AI agents as autonomous, LLM-based applications capable of planning, tool usage, and dynamic adaptation. The presentation emphasizes that digital assets should evolve to serve as tools or agents themselves, enabling capabilities like multi-step reasoning, adaptability, and computer vision for automation.