The reason diverse tech products from Linear to Notion are building similar AI agent capabilities is the emergence of a "general harness" architecture. This common pattern—a loop of context engineering, model calls, and tool usage—is a general-purpose framework for solving problems, leading to a convergence of product features across different domains.

For its Custom Agents feature, Notion rejected the goal of making it "as easy as possible to use." They realized simplifying the interface would abstract away critical interpretability and diminish the tool's power, so they aligned on building a deep, sophisticated product for "the top of the class."

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Notion is creating a new, defensible market by positioning its platform not just for human work, but as a central hub where different third-party AI agents can interact, collaborate, and have their actions tracked. This strategy aims to make Notion the essential infrastructure for an emerging agent-driven workforce.

Notion's core vision has fundamentally changed because of AI. The co-founder explained their goal shifted from building the best tool for humans to *directly perform* work, to creating the best platform for humans to *manage agents* that do the work for them, using the same core primitives like pages and databases.

Companies must now design their products, from documentation to onboarding, for a new primary user: the AI agent. This "Agent Experience" (AX) is critical because agents are how a new, massive user base will interact with and build upon platforms, making it a product's North Star.

For tools designed for AI interaction, the ease with which an agent can use the product (AX) is as critical as the user experience (UX) for humans. This can be improved by directly asking the agent for feedback on how to make the product more ergonomic for it.

The PM role will expand beyond leveraging off-the-shelf AI. They will be responsible for creating and training specialized AI agents. This involves instilling agents with deep, company-specific knowledge of business models, customers, and strategy, just as they would onboard a new human team member.

Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.