Most AI coding tools are benchmarked on capability. But capability doesn't predict adoption. At Kilo Code, we've processed over 25 trillion tokens across 1.5M+ developers, giving us one of the clearest real-world datasets on how engineering teams actually integrate AI into their workflows. This talk breaks down the adoption ladder we've observed (autocomplete → chat → single agents → orchestration), where developers stall out, and the three technical failure points that consistently kill trust before agents ever get a real chance: context construction, model routing, and feedback loops. I'll walk through what the data shows about how context windows need to scale at each rung, why no single model wins across task types, and how trust itself becomes a measurable signal you can design around. If you're building AI-powered dev tools or trying to roll out agents across an engineering org, this is the stuff benchmarks won't tell you.
Speaker
Brian Turcotte
Developer Relations Engineer @Kilo Code
Brian Turcotte is a Developer Relations Engineer @Kilo Code. I facilitate product marketing launches, educational videos, technical walkthroughs, and written/visual content across our blog, social media, and website. I also collaborate with AI model partners on promotional events and work to improve our documentation and technical resources. On the community side, I facilitate live webinars, discussions, and meetups that bring together Kilo users and the broader open-source community. It's one of the most rewarding parts of the job - connecting with developers who are pushing the boundaries, and learning from how they're using our tools in the real world.
