Many organizations would be hesitant to overhaul their tech stack and start from scratch.
Not Notion.
For the 3.0 version of its productivity software (released in September), the company didn’t hesitate to rebuild from the ground up; they recognized that it was necessary, in fact, to support agentic AI at enterprise scale.
Whereas traditional AI-powered workflows involve explicit, step-by-step instructions based on few-shot learning, AI agents powered by advanced reasoning models are thoughtful about tool definition, can identify and comprehend what tools they have at their disposal and plan next steps.
“Rather than trying to retrofit into what we were building, we wanted to play to the strengths of reasoning models,” Sarah Sachs, Notion’s head of AI modeling, told VentureBeat. “We’ve rebuilt a new architecture because workflows are different from agents.”Re-orchestrating so models can work autonomouslyNotion has been adopted by 94% of Forbes AI 50 companies, has 100 million total users and counts among its customers OpenAI, Cursor, Figma, Ramp and Vercel.
In a rapidly evolving AI landscape, the company identified the need to move beyond simpler, task-based workflows to goal-oriented reasoning systems that allow agents to autonomously select, orchestrate, and execute tools across connected environments. Very quickly, reasoning models have become “far better” at learning to use tools and follow chain-of-thought (CoT) instructions, Sachs noted. This allows them to be “far more independent” and make multiple decisions within one agentic workflow. “We rebuilt our AI system to play to that,” she said.
From an engineering perspective, this meant replacing rigid prompt-based flows with a unified orchestration model, Sachs explained. This core model is supported by modular sub-agents that search Notion and the web, query and add to databases and edit content.
Each agent uses tools contextually; for instance, they can decide whether to …