From Coding to Context Switching: An AI Retrospective
In this episode, Deejay welcomes back Elliott Beatty to discuss the long-term reality of adopting agentic AI coding assistants. While development velocity has soared, the team now faces 'human' challenges like burnout and context-switching fatigue. Elliott explains why frontend teams benefit more than backend engineers and how accelerated coding exposed major bottlenecks in QA and UAT. They also explore the shift toward Model Context Protocol (MCP) servers, the necessity of feature flags, and why strong leadership is crucial for sustainable AI implementation.
Episode Transcript
Deejay (00:02) This is the Waves of Innovation podcast and I am DJ your host in this episode. I am talking to Elliot Beatty who long time listeners of the podcast might recognize. We are catching up with his journey of agentic AI coding assistant adoption within his organization. In this episode, we...
Episode Highlights
- •Velocity increased significantly, but developer burnout emerged due to excessive context switching.
- •Frontend teams see massive gains while backend teams struggle with complex system logic.
- •Accelerated coding speed caused major logjams in QA and User Acceptance Testing.
- •AI agents excel at rote tasks but fail at high-level architectural problem solving.
- •Moving to Model Context Protocol servers will eventually replace traditional API interactions.
- •Implementing feature flags became essential to manage the high volume of unreleased code.
- •Successful AI adoption requires executive leadership to manage friction across all departments.
