Foreword · The Agentic Awakening

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The Agentic AwakeningHow I came to this work<br>Key Takeaways

IBuild the Churches

IIConvert the People

IIIAssemble the Community

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Foreword<br>The Agentic Awakening

From the Author

How I came to this work.

About three years ago, after more than twenty years in CEO seats running software companies, I<br>finished my last role and finally had time on my hands. I found myself completely absorbed by the<br>generative-AI revolution that was just starting – reading and analyzing almost every piece of<br>writing that came out, and not only on the technical side. The social, historical, political, and<br>economic implications fascinated me at least as much as the models themselves. The whole shape of<br>what AI was about to do to the world had me in a state I hadn’t been in for years.

But the further I went, the more one specific itch kept returning. I had always believed that<br>software development was the area where AI’s future would be most visible first, because it was<br>already the most advanced area of AI usage. If I really wanted to understand where this was<br>going, I needed to build. There was just one problem: I had not written<br>production code in a very long time. Different languages, different runtimes, a different<br>operating model entirely. I felt completely out of shape. But the thought kept returning –<br>why can’t I do it?

So one day I just sat down and dove in. The first move was small: experience what it felt like<br>to work inside an agentic IDE. Within hours I was addicted. That experience became the first<br>platform I built, AI-assisted from end to end – letting me move on things I hadn’t touched in years.

The second platform, just one year later, was already purely agentic: I directed intent and<br>reviewed output; the code wrote itself. By that point I had forced myself through every serious tool in the space, and the agent harnesses layered on top of them, comparing them on real work<br>rather than on benchmarks. I was running multiple<br>agent sessions in parallel and routinely generating hundreds of thousands of lines of production<br>code per month, operating in a mode that had no analog in how software was built even two years<br>earlier – let alone two decades. In the gap between those two builds I watched the shift from 2024 to 2025 happen in<br>real time. The whole way software gets built was changing under me. That was when I felt<br>AI-pilled.

I use the word addicted deliberately. The pull is the<br>same one that hooked me on programming in the first place, decades ago – you type something, the<br>machine obeys, and a small jolt of it worked, a hit of dopamine, lands before you’ve thought<br>about what comes next. Agentic coding turns that hit into a slot machine: you write a sentence, the<br>agent disappears for a minute, and comes back with a working feature, or a near-miss that’s one more<br>prompt from working. The reward is variable and the friction is gone, the same loop that makes a video<br>game hard to put down. Every win suggests three more things to try, and the cost of trying is a<br>sentence. I lost more than one night to just one more prompt, and I was nowhere near alone.

When Adam Fisher from Bessemer Venture Partners approached me about this project, I saw an opportunity to study<br>the same shift from the other side of the table – across their portfolio and beyond, at scale – and to put<br>two perspectives in the same frame: the hands-on experience I had been accumulating as an<br>AI-native builder, and twenty-plus years of running software companies. The combination<br>let me ask sharper questions than either viewpoint alone would have allowed. This playbook is<br>the result.

Across our conversations with portfolio companies, four patterns kept appearing. Some teams<br>believed they were making progress but had no benchmark against which to measure it. Others<br>were moving quickly but kept their methods close, making it difficult to compare approaches.<br>Some were spending heavily on AI without seeing the investment translate into output. And some<br>knew what needed to change but could not get the organization to move. We realized the field<br>needed more than another report: it needed a practical guide for founders, CEOs, engineering<br>leaders, and boards navigating the transition to agentic engineering.

The work behind this document

During the first half of 2026, we spoke in depth with CTOs and engineering leaders from more than<br>twenty companies, including Ramp, Lemonade, Wonderful, and DriveNets. They ranged from small<br>AI-native startups and mid-stage software companies to late-stage organizations with codebases<br>over a decade old. The study spans consumer software, B2B<br>SaaS, infrastructure, security, e-commerce, marketing, and several other verticals – broadly enough<br>that the patterns are not specific to any one industry.

The interviews ran in successive rounds, with findings synthesized between each. The framework<br>barely needed to change as the...