Remote MCPs: What we learned from shipping — John Welsh, Anthropic

This talk explores the complexities of transitioning a SaaS company to an AI-first model, emphasizing that it's an evolutionary journey rather than a binary switch. It proposes a framework focusing on strategy, ways of working, and people to navigate this transformation. The core thesis is that becoming AI-first requires reimagining product development, embracing ambiguity, and fostering a cultural shift across the entire organization, not just within AI teams.

The Model Context Protocol (MCP) is an open-source, standardized protocol designed to give AI models agency by allowing them to interact with the outside world. Originating from the need to copy context from external sources into LLM context windows, MCP aims to enable models to reach a new level of usefulness and intelligence by facilitating tool calling and broader interaction capabilities. The protocol prioritizes server simplicity and encourages community contributions to evolve its standards and utility.

This talk focuses on building production-ready AI agents, moving beyond simple prototypes to scalable, cloud-hosted solutions. It outlines the essential components of an agent, including the model, prompt, loop, history, and tools, and demonstrates how to leverage AWS services like Amazon Bedrock Agents to deploy these agents at cloud scale. The presentation emphasizes practical steps for integrating custom tools via Lambda functions and preparing agents for a production environment.

This talk argues that the future of software development is agent-driven, moving beyond human-driven approaches. It posits that AI agents, when properly integrated and provided with sufficient context, can handle a majority of tasks across the software lifecycle, enabling production software to be built in minutes rather than months. The core thesis is that organizations must transition to an agent-native development paradigm to unlock the true power of AI.

This talk introduces an evaluation framework for AI product managers (PMs) focused on shipping reliable AI applications. It emphasizes the critical need for robust evaluation, analogous to software testing but adapted for the non-deterministic nature of AI models. The framework aims to provide PMs with tools and methodologies to build confidence in AI product performance, moving beyond subjective "vibe coding" to data-driven "thrive coding."