From Coder to Manager: Navigating the Shift to Agentic Engineering with Notion Co-Founder Simon Last

What It Covers

Notion Co-Founder Simon Last describes how Notion rebuilt its AI architecture roughly every six months, launched personal and custom agents in 2024, and shifted the engineering role from writing code to managing agents that autonomously execute, verify, and deploy end-to-end tasks across workspaces.

Key Questions Answered

• •AI Harness Cadence: Rebuild AI system architecture approximately every six months rather than maintaining a single implementation. Model capabilities advance fast enough that a harness designed around last year's models underperforms. Notion is currently deploying a new harness while already designing the next one, treating rewrites as a standard part of the product cycle.
• •Agent-Optimized APIs: Default APIs built for humans perform poorly with agents. Notion replaced its verbose JSON block format with a custom Markdown dialect for pages and switched to SQLite syntax for database queries. Identify which data formats models are already trained on and design agent interfaces around those priors rather than repurposing human-facing endpoints.
• •Memory-Loop Agent Training: Build agents with a writable memory page, run them in approval mode for several days, and correct their outputs interactively. The agent converts corrections into explicit rules. After roughly two weeks of iteration, Last removed human approval entirely from his email-triage agent, which now autonomously archives messages using hundreds of self-generated routing rules.
• •Verification-First Coding Workflow: Effective agent-assisted engineering requires defining the change, the verification method, and the safe deployment path before prompting. Last no longer writes code manually, instead specifying end-to-end tasks and acting as outer verifier. This approach produces larger, more complex pull requests but with full end-to-end test coverage on every submission.
• •Retrieval Quality Requires Source-Specific Tuning: Semantic indexing across different data sources — Slack, Google Drive, Notion — demands separate chunking strategies and retrieval pipelines per source. A single universal approach degrades performance. Notion iterates continuously by running real queries daily against each source, treating retrieval tuning as ongoing craft work rather than a one-time configuration decision.