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: [email protected]: re-cinq.com

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Why do AI projects feel so unpredictable? If you've ever been frustrated by a machine learning project that shows "nothing, nothing... then suddenly, something," this episode is for you. We move past the hype to explore the "fuzzy" reality of building with AI and why it’s a fundamentally different discipline from traditional software engineering.

Discover why you can't just apply Agile sprints to ML development and why models need to be "massaged and babied" rather than simply "built." We break down the practical engineering challenges of MLOps, from the difficulties of testing non-deterministic systems (where "pass/fail" doesn't apply) to the complexities of safely deploying them.

We also go deep on Reinforcement Learning (RL), a powerful but high-stakes branch of AI. You'll learn:

• What makes RL unique (its "agency to explore").

• Why live testing in industrial settings "could be catastrophic."

• How "Offline RL" allows us to train powerful agents from simple log files.

• The surprising role RL plays in training the large language models (LLMs) we use every day.

Tune in for a practical, engineering-focused look at what it really takes to get AI from a concept to a reliable, production-ready product.

How do you implement cutting-edge AI in one of the world's most regulated and secure environments?

In this episode, Deejay talks to Mike Gehard, Director of R&D at Rise 8, a custom software development firm that is all-in on developing AI solutions for the US federal government. Mike shares how his team is tackling the "nightmare difficulty" of using generative AI for both internal software development and for building AI-powered features for government agencies.

This is a masterclass in pragmatic AI adoption, moving beyond the hype to solve real-world security, process, and human challenges. Mike details the practical steps Rise 8 is taking, from running Claude Code securely in containers to his ambitious goal of enabling a single person to take a feature from idea to production.

In this episode, you will learn about:

• Practical strategies for using AI securely in highly regulated industries like government, finance, and healthcare.

• Why Mike believes "inside-out" test-driven development is dead in the age of AI.

• The power of custom slash commands and MCP servers to amplify developer effectiveness and share knowledge.

• How to address "AI hesitancy" and encourage adoption among skilled software craftspeople.

• The story of the "Brian Bot" and what it reveals about AI's powerful pattern-matching capabilities.

• Mike’s vision for a single person to take a product from idea to production using AI.

About Our Guest:Mike Gehard is the Director of R&D at Rise 8, a custom software development firm focused on delivering AI-powered solutions for the US federal government. With a background as a software craftsman at Pivotal, Mike is now focused on transforming the software development lifecycle by integrating AI at every step, navigating complex security requirements to deliver value to the US taxpayer.

In this episode, we sit down with Nick Gushchin, founder of the Swiss AI Chatbot Factory, to hear his incredible story of career transformation. After nearly 18 years in the banking industry, Nick embarked on a journey to teach himself Python, using ChatGPT as his primary tutor.

Nick shares how a chance conversation on an airplane introduced him to the coding capabilities of ChatGPT 3.5, sparking a realization that he could build his own projects much faster than the 9-12 month timelines he was used to in banking. This led him to take a sabbatical, dedicate himself to learning, and eventually develop over 20 of his own pet projects.

Discover how one of these projects, an AI chatbot on Telegram, gained over 100,000 users and became the catalyst for him to leave banking for good. Nick also details the founding of the Swiss AI Chatbot Factory, a platform that uses AI agents to automate the creation of smart, reliable chatbots in under three minutes. He explains the multi-layered approach his factory uses to prevent AI "hallucinations" and ensure chatbots provide accurate, context-aware answers without being "dumb".

This is a must-listen for anyone interested in AI, career changes, and the power of leveraging new technology for rapid innovation.

What's the most important skill for building reliable AI? It might not be what you think.

In this episode, we sit down with Qamir Hussain, Head of AI at Arisa (formerly of Webio), who shares incredible insights from building AI for the high-stakes world of debt collection. He argues that a QA mindset is more critical than any other skill for an engineer in the AI space.

We dive into the real-world war stories of developing AI-driven tools that assist agents in navigating difficult and sensitive financial conversations.

In this episode, you'll learn:

Tune in for a masterclass in the practical engineering and strategic thinking required to make AI work in the real world.

Feeling overwhelmed by the new wave of AI jargon? You're not alone. In this episode, we're joined by Hannah Foxwell, a tech leader who, after feeling "bamboozled" at an AI conference, founded the "AI for the Rest of Us" community. Her mission: to help everyone, not just the deep experts, develop the "AI fluency" needed to navigate this new era.

Hannah draws fascinating parallels between the current messy, opportunity-rich moment and the early, culture-driven days of the DevOps movement. In a candid conversation, she argues that the era of "coddled developers" is over and that the industry's focus must shift from simply writing code to delivering measurable business impact. She warns that without this cultural shift, AI's productivity gains will only turn us into "AI-powered feature factories," doing the wrong things faster than ever before.

So, how do we move forward? Hannah offers practical patterns for leaders and engineers, from the critical role of "enabling teams" (citing Team Topologies) to the non-negotiable need for "slack in the system" to foster real innovation. This is a must-listen for anyone looking for an honest, hype-free guide to the cultural, career, and strategic challenges of the AI Native wave.

What would it take to have your engineering team write almost no code by the end of 2026? That's the audacious goal tech leader Elliot Beaty set for his team after realizing the industry was at a critical inflection point. In this episode, Elliot takes us deep inside his journey of transforming a development team into an AI-first powerhouse, detailing the pivot that began with reluctance 3 and has led to a fundamental shift in how they build software.

The results have been staggering: a nearly 250% increase in team velocity, even while tackling more complex features. Elliot shares the landmark moment when an AI agent fixed a complex, two-day bug in just 30 minutes, all initiated from a single Jira ticket. He also reveals the surprisingly crucial role that high-quality documentation now plays, providing the essential context that AI agents need to succeed and turning a once-dreaded task into a high-value activity.

But this transformation isn't just about productivity gains. Elliot offers a hype-free look at the real-world challenges, from the "multitasking overload" and mental fatigue caused by managing AI agents to the unexpected bottleneck this new speed has created for his QA team. Learn why current AI is still largely "monkey see, monkey do," and how his team is wrestling with agents that often build

This conversation is an essential playbook for any technology leader, manager, or engineer. Tune in for a practical discussion on adoption strategies, the evolving role of the senior developer, and the potential engineering skills gap we may face in the coming decade.

Welcome to the first episode of Waves of Innovation.

This podcast, and the community around it, started with a simple question:
What if we could approach the next technological transformation differently this time?

In this opening conversation, Pini Reznik (Author of Cloud Native Transformation: Practical Patterns for Innovation) shares the story behind his upcoming book Waves of Innovation, the thinking that shaped it, and why the shift toward AI Native is about much more than just a passing trend.

Together with your host Deejay, he reflects on a decade of helping companies navigate their Cloud transformations, and what we need to do differently in this next wave. Pini emphasizes that if you ignore this wave now, you will soon suffer from the repurcussions.

If you're curious about the ideas behind the book, the meaning, or the people shaping this movement, this is where it begins. And we’re only getting started.

Have thoughts, suggestions, or feedback?

Reach us anytime at: [email protected]