Sarah Chieng (Cerebras) and Cova Lee (OpenAI) walk through an agent-powered content system spanning ideation, scripting, recording, editing, repurposing, and publishing, emphasizing swipe files, voice-driven scripts, design systems, API-based editing, and weekly process reviews as ways to free creators to focus on craft over generic AI output.
Gumloop co-founders Max Brodeur-Urbas and Rahul Behal discuss how a visual workflow builder evolved into an enterprise agent platform, what they learned from more than 1,100 customer calls, how a power user at Instacart opened the door to enterprise sales, and why the people who understand a task should be the ones automating it. The conversation covers enterprise requirements, pricing transparency, model neutrality, and the IT-led buying dynamics that shape adoption.
Aaron Levie, CEO of Box, details his company's successful pivot to AI, emphasizing the critical role of application companies in bridging raw model capabilities to complex enterprise workflows. He argues that significant value will accrue to "applied AI" solutions that understand domain-specific needs, can handle intricate data permissions, and build specialized agents, rather than relying solely on foundational models. Levie shares insights on the economics of tokens, the rise of open-weight models, and why AI diffusion in coding differs from other knowledge work, predicting that 90% of enterprise tokens will soon be consumed by non-human initiated tasks.
Brandon Waselnuk of Unblocked discusses the critical need for robust context engines in AI-driven software development. He explains how AI agents' lack of company-specific understanding leads to compounding costs like wasted tokens and rework. Waselnuk outlines common pitfalls in providing context and details six essential characteristics of an effective context engine, demonstrating its impact with a 50% token reduction and significant time savings. He also introduces three open-source tools from Unblocked, including a workshop to build a relational context engine, emphasizing that "access to information is not understanding" and that context, not intelligence, is the current bottleneck for AI effectiveness.
Figma's internal AI agent adoption journey faces challenges like reduced developer agency, skepticism from senior engineers, and communication inefficiency. Solutions include investing in verification, using a testing pyramid for agent review, prioritizing detailed planning over prompting, engaging skeptics to build AI safety roadmaps, and implementing attention-aware communication by clearly marking AI-generated content.
DoorDash's GenAI platform team transformed evaluation from an engineering-centric task into a cross-functional workflow. By adopting an API-first strategy, they empowered non-engineers like Strategy & Operations to "vibe code" their own annotation UIs using coding agents and self-serve calibrate LLM judge prompts. This approach dramatically reduced annotation costs, accelerated iteration, and fostered broader organizational ownership of AI quality.