Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks

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.

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 critically examines the Model Context Protocol (MCP) ecosystem, arguing that despite initial excitement and the promise of standardizing AI agent interactions with services, significant challenges remain. The speaker contends that the current state of MCPs is fragmented and difficult to use, hindering the practical application of autonomous agents. The core thesis is that while the potential for AI agents is vast, the MCP infrastructure is not yet mature enough to fully realize this potential, necessitating further development and standardization.

This talk explores strategies for effectively integrating and managing third-party tools within AI agent workflows, specifically focusing on Multi-Call Protocol (MCP) servers. It addresses the common challenges of generic tool descriptions, unexpected agent behavior, and potential performance degradation or security risks when using off-the-shelf tools. The presentation outlines a framework of five best practices to tailor these tools for specific use cases, ensuring agents perform as intended and applications remain stable.

This talk explores how to move beyond traditional code coverage metrics to ensure true functionality testing, especially in the context of AI-assisted development. It argues that while AI can increase code output, its true productivity gains are amplified by clean codebases and robust testing. The presentation introduces Playwright as a tool for end-to-end browser testing, demonstrating how it can be integrated with AI agents to accelerate development workflows, particularly within a test-driven development (TDD) framework.