Notion’s simplified AI agent feature feels indispensable, says engineer
When Notion rolled out its new AI agent, most users treated it as a neat add‑on. For software engineer Nystrom, however, the shift felt more like a rewrite of his daily workflow. He’d spent years building “extremely deterministic” tools, where every input produced a predictable output.
Then a teammate suggested he phrase his prompts the way he would explain a task to a human. That simple change sparked what Nystrom calls a “light‑bulb moment.” The feature stripped away layers of code‑like syntax, letting him ask the AI in plain language and get back organized notes, project outlines, or even code snippets without fiddling with settings. In the weeks that followed, the agent stopped being a curiosity and became a core part of how he structures ideas.
The transition illustrates Notion’s broader push to make AI feel less like a separate module and more like an integrated teammate. It’s this evolution that leads many users to wonder whether they could ever go back to a Notion without it.
Now it's, 'How could I ever use Notion without this feature?'" 'Rewiring' for the AI agent era As a traditional software engineer, Nystrom was used to "extremely deterministic" experiences. But a light bulb moment came when a colleague advised him to simply describe his AI prompt as he would to a human, rather than codify rules of how agents should behave in various scenarios. The rationale: LLMs are designed to understand, "see" and reason about content the same way humans can. "Now, whenever I'm working with AI, I will reread the prompts and tool descriptions and [ask myself] is this something I could give to a person with no context and they could understand what's going on?" Nystrom said on the podcast.
Is simplicity the secret? Notion’s AI team discovered that stripping away complex schemas and heavy instructioning yielded a feature that engineers now describe as indispensable. By replacing intricate code generation with human‑readable prompts and markdown, the model’s performance jumped dramatically, according to engineering lead Ryan Nystrom.
The shift from deterministic engineering habits to a more conversational prompt style appears to have paid off, yet whether this minimalist approach will scale across other use cases remains unclear. The team’s pivot underscores a broader lesson: heavy abstraction isn’t always necessary for effective AI assistance. Still, the article offers no data on long‑term user retention or comparative benchmarks, leaving open questions about durability.
In practice, users report a feeling that “how could I ever use Notion without this feature?” reflects genuine utility, but the lack of quantitative metrics tempers enthusiasm. Ultimately, the case study highlights how a focused redesign can produce noticeable gains, while also reminding us that broader validation is still pending.
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Common Questions Answered
How did Nystrom's approach to prompting the Notion AI agent differ from his previous deterministic tools?
Instead of encoding strict rules, Nystrom began describing tasks in natural, human‑readable language, similar to how he would explain them to a colleague. This conversational style let the large language model interpret intent more flexibly, moving away from the rigid, code‑like syntax he previously used.
What impact did using human‑readable prompts and markdown have on the performance of Notion’s AI agent?
According to engineering lead Ryan Nystrom, replacing intricate code generation with simple, markdown‑based prompts caused the model’s performance to jump dramatically. The simplification reduced instruction overhead and allowed the AI to reason more effectively, making the feature feel indispensable to engineers.
Why does the article suggest that simplicity might be the secret behind Notion’s AI agent success?
The Notion AI team discovered that stripping away complex schemas and heavy instructioning resulted in a more usable feature. By focusing on straightforward, conversational prompts, engineers experienced a smoother workflow and higher productivity, indicating that minimalism can enhance AI usability.
What does the term 'light‑bulb moment' refer to in the context of Nystrom’s experience with the Notion AI agent?
The 'light‑bulb moment' describes when a teammate advised Nystrom to phrase his prompts as if speaking to a human, which instantly clarified how to interact with the AI. This realization shifted his mindset from deterministic coding to leveraging the LLM’s natural language understanding capabilities.