David Luan on agent engineering
From David Luan: DeepSeek’s Significance, What’s Next for Agents & Lessons from OpenAI · · Unsupervised Learning: Redpoint's AI Podcast
“To turn a base multimodal model into a large-action model you have to solve two things: an engineering problem — expose in a model-legible way what it can do (APIs, UI affordances) — and a research problem — teach it to plan, reason, replan and follow user instructions.”
On , David Luan, CEO & Co-Founder at Adept AI, spoke about agent engineering during David Luan: DeepSeek’s Significance, What’s Next for Agents & Lessons from OpenAI on Unsupervised Learning: Redpoint's AI Podcast.
About David Luan
David Luan, CEO and co-founder of Adept AI, has been discussing the company's focus on building AI agents for enterprise knowledge workers. He has described Adept's approach as training a foundation model that can translate natural language into actions on a computer, rather than competing directly with general-purpose LLM providers. Luan has stated that the company is "very enterprise focused" and is working on larger engagements where Adept agents can accelerate knowledge work. He has also emphasized that Adept controls both its own foundation model for agents and the product enterprises use, describing this as a bet on vertical integration. In various interviews, Luan has shared his views on the broader AI landscape. He has argued that the business of training large base models is "quickly commoditizing" and that companies in that space will need to find alternate ways of making money beyond having better models. Luan has also commented on the importance of reliability for agent products, stating that if an agent makes operational errors a third of the time, people will stop using it. He has predicted that AGI is "really not super far away" but that it will not diffuse through society quickly due to other bottlenecks. Luan has also expressed concern about regulatory capture, saying that lawmakers' lack of understanding of the technology makes it easier for those with ulterior motives to shape policy.