Stripe Kai
Like many other companies, Stripe’s software engineers have been taking advantage of AI agents for a while. But what about other knowledge workers? For them, the company built a bespoke Knowledge AI platform, known as Kai. This post explains the system’s architecture and early outcomes. It’s a blueprint for organizations looking to build similar systems. (And they should be.)
Taxonomies and knowledge management
Heather Hedden on how taxonomies and ontologies support enterprise knowledge management systems. If you’re looking to augment your team using agents, they’ll need shared organizational knowledge. Metadata helps such systems work more effectively, so taxonomies and ontologies are more important than ever.
Planning with agents
Planning is essential for effective agentic work. Alas, current human-computer interfaces don’t lend themselves to effective planning. In this presentation, Maggie Appleton shares explorations of more effective human-agent planning. The key insight: leaders should set up boundary objects that support human judgment when AI is in the mix.
Composable AI design
This is from January of this year, so perhaps it classifies as “oldie but goodie.” Jeff Veen calls for a reevaluation of experience design based on agentic capabilities, not unlike that which happened fifteen years ago when teams had to shift to thinking mobile-first. This time around, the primitives are command-line apps — a modular architecture that harkens back to the “small pieces, loosely joined” Unix philosophy.
The inference revolution
Most people don’t think about the technology underlying AI. When they do, I bet most think about training. But that’s not the only part. Inference matters too. This in-depth article explores the current state of inference. It serves as a reminder that it’s still early days for AI. My read: invest in flexible, modular architectures that let you swap out components as more efficient solutions become available. (H/t Emily Campbell)