
From Coder to Manager: Navigating the Shift to Agentic Engineering with Notion Co-Founder Simon Last
Sarah Guo (host), Simon Last (guest)
In this episode of No Priors, featuring Sarah Guo and Simon Last, From Coder to Manager: Navigating the Shift to Agentic Engineering with Notion Co-Founder Simon Last explores notion’s shift to agentic work: indexing, APIs, and teams Notion’s AI journey began with early GPT-4 access in 2022, leading to a fast launch of an AI writing assistant and a longer push toward a general-purpose workspace agent.
Notion’s shift to agentic work: indexing, APIs, and teams
Notion’s AI journey began with early GPT-4 access in 2022, leading to a fast launch of an AI writing assistant and a longer push toward a general-purpose workspace agent.
A major technical inflection point was building high-quality semantic indexing and retrieval across Notion and third-party sources (e.g., Slack, Google Drive), requiring empirical iteration on chunking, pipelines, and evals.
Notion repeatedly rewrites its “AI harness” roughly every six months to match rapid model progress, and the rise of coding agents has made larger rewrites and more ambitious PRs feasible.
Notion’s product direction is now “tool for humans” plus “tool for humans managing agents,” including personal agents, newly launched custom agents, and agent-friendly APIs (markdown-like pages + SQLite for databases).
Key Takeaways
Ship short-term wins while building toward an agentic north star.
Notion paired a quick-to-deliver writing assistant with a longer-term bet on a general assistant that can use all Notion tools; the latter required years of iteration until models and harnesses caught up.
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Indexing and retrieval quality is mostly craft plus relentless empiricism.
Simon argues many companies underperform on search because they don’t iterate empirically; each data source (Slack vs Drive) needs tailored retrieval tactics, chunking, and continuous tuning.
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Embeddings reduce dependence on users’ folder/tree organization.
Because retrieval can work from semantic snippets, Notion increasingly advises users not to over-optimize workspace structure—focus on getting information into the system so it can be retrieved.
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AI systems demand frequent rewrites to stay aligned with model capabilities.
Notion “rewrites the AI harness” about every six months, treating it as necessary product engineering rather than tech debt, because model behavior and best practices change quickly.
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Coding agents increase ambition—but only with strong verification loops.
Simon distinguishes robust agent-driven development (clear specs, tests, safe deploys) from “slop”; PRs are bigger, but expectations for end-to-end testing and review rise accordingly.
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The performance gap between engineers will widen with tool adoption.
He claims the minimum bar may be similar, but the ceiling expands dramatically for those who can effectively orchestrate agents—leading to more prototypes, more chaos, and faster iteration.
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Agents are a new ‘API customer’ requiring different interfaces.
Notion found its existing verbose block JSON was token-inefficient and agent-hostile, so it built agent-optimized primitives: an enhanced markdown dialect for pages and SQLite for database queries.
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Notable Quotes
“We rewrite our AI harness probably every six months or so.”
— Simon Last
“You can be like 100 or 1000X engineer if you're using the tools right now.”
— Simon Last
“If you do it badly, it's all slop.”
— Simon Last
“We see ourselves as kind of like the Switzerland for models.”
— Simon Last
“Before AI, our goal was to create the best tool for humans to directly perform their work. And then now the goal is to create the best tool for humans to manage agents to do the work for them.”
— Simon Last
Questions Answered in This Episode
On the retrieval side, what specific chunking and re-ranking strategies ended up working best for Slack vs Google Drive, and what failed in early iterations?
Notion’s AI journey began with early GPT-4 access in 2022, leading to a fast launch of an AI writing assistant and a longer push toward a general-purpose workspace agent.
Get the full analysis with uListen AI
What does Notion’s “AI harness” concretely include (prompting layer, tool router, eval suite, logging/telemetry, safety checks), and what changes most from rewrite to rewrite?
A major technical inflection point was building high-quality semantic indexing and retrieval across Notion and third-party sources (e. ...
Get the full analysis with uListen AI
You mentioned agent-friendly APIs: what does your enhanced markdown dialect look like, and what were the key design constraints to keep it both expressive and token-efficient?
Notion repeatedly rewrites its “AI harness” roughly every six months to match rapid model progress, and the rise of coding agents has made larger rewrites and more ambitious PRs feasible.
Get the full analysis with uListen AI
Custom agents run autonomously in the background—what guardrails exist to prevent accidental destructive actions (e.g., mass edits, misfiled tasks) as users drop approval mode?
Notion’s product direction is now “tool for humans” plus “tool for humans managing agents,” including personal agents, newly launched custom agents, and agent-friendly APIs (markdown-like pages + SQLite for databases).
Get the full analysis with uListen AI
Why choose SQLite as the interface for database interactions—what tradeoffs did you consider versus a JSON query DSL or natural-language-to-query only?
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Transcript Preview
[upbeat music] Hi, listeners. Welcome back to No Priors. Today, I'm here with Simon Last, co-founder at Notion. We talk about their new vision for Notion in the AI age as a platform for humans and agents to collaborate, how the engineering and product org at Notion is changing, and these new tools for thought. Welcome, Simon. Hey, Simon. Thanks for doing this.
Yeah, of course. Yeah, it's really fun to be here.
Notion's at scale, amazing platform, lots of users. You did start quite a while ago. I think of Notion as one of the companies that has really, like, raced AI quite aggressively. I was told you first got your hands on GPT-4, uh, at a company off-site in Mexico. Um, is that true? What is the origin story of, like, starting to work on this stuff?
Yeah, I think... Yeah, that year, that was twenty twenty-two. Um, I, I've been watching, you know, what's going on. In general, I've just been, like, super curious about the technology and fascinated to, to try everything and think about, like, like how we can apply it. It wasn't until I played with GPT-4 that it, it became really, really real. So, you know, we-- When we got access to it, it wa- it was sort of like a, a proto-ChatGPT-like interface. Um, and, uh, my co-founder Ivan and I both, both got access, and it was just immediately clear, like, I would say two big things. One is that it was just pretty smart. It c- it, it could follow reasonably complicated instructions. It could write things for you. It could edit things. And, and the second big thing was that, uh, uh, the scope of its knowledge was extremely interesting. Uh, super, super deep, like, um, and, and broad world knowledge. When we played with it, it became just instantly clear to both of us, like, okay, the, the time is now to start th- thinking about how to apply this. It's only gonna get better.
We were talking about Mexico, GPT-4. You guys saw it was, like, clearly the time. Did you start with, like, a particular vision of, like, what you should obviously be able to do with AI and Notion? Or did you start pulling people from different teams or recruiting people and say, like, "Let's experiment"? How did you begin?
I think we immediately had a long-term and a short-term vision. I would say the, the, uh... I'll start with the short-term one. The, the thing that was immediately obvious was, oh, it could be, like, a writing assistant.
Mm-hmm.
Um, so it, it could be in your document. You can, like, select some text, have it rewrite it. You can have it write text for you, maybe look something up, and then, uh, you know, give you, like, like, sources or more information. So that was the thing that we immediately, like, like, got to work on, and, you know, we sort of started a tiger team around it, and then we were able to launch it in, like, two, three months after that. And then the long-term vision that we immediately had was like, "Oh, the thing that looks like it may be possible is more of, like, a general assistant." So what if you could just give it all the tools inside Notion that a human would have, be able to, like, create its own databases, query, manipulate them, create documents, edit them, uh, and sort of weave all of these things together to do, like, a longer range task. And so we, we sort of, uh, immediately started on both. The, the short-term one, we were able to ship very quickly, and then the long-term one didn't really work yet, and so that took much longer to get working.
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