What happens after you go "all in" on AI coding assistants?
In this episode, Deejay catches up with Elliott Beatty, host of the Agentic CTO podcast, and VP of Engineering at Fruition, to review his organization’s aggressive adoption of agentic AI. Several months ago, the goal was full automation. Today, the reality is more nuanced. While velocity is up tremendously, the team has hit a new ceiling: the human element.
Elliott pulls back the curtain on the unintended consequences of hyper-productivity, including developer burnout, "context switching" fatigue, and the massive bottlenecks created in QA and User Acceptance Testing (UAT) when code is written faster than it can be checked.
In this episode, we cover:
The Human Cost of Velocity: Why running multiple agents simultaneously led to engineer burnout and mandatory time off.
Frontend vs. Backend: Why AI agents excel at React and Flutter "monkey-see-monkey-do" tasks but struggle with complex backend microservices and scalability logic.
The "Logjam": How a 100% increase in coding speed exposed critical weaknesses in QA, UAT, and stakeholder approval processes.
Tooling Shifts: Why the team ditched Jira for Linear, embraced Model Context Protocol (MCP) servers, and the critical importance of Feature Flags (LaunchDarkly) in an AI-driven workflow.
Leadership Advice: Why you need an "AI Quarterback" to manage the friction between engineering, product, and marketing.
Tools & Resources Mentioned:
Qase: The test management tool mentioned by Elliott (Qase.io).
Linear: For issue tracking with AI integrations.
Granola: For AI note-taking and meeting summaries.
N8n: For workflow automation in QA.
Cursor / Windsurf / Claude Code: The current stack of coding assistants.
Contact & Feedback:Have you experienced AI burnout in your team? Let us know.Email: wavesofinnovation@re-cinq.comWebsite: re-cinq.com
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