When building AI-driven workflows, the primary interface becomes the API, not the GUI. A tool's value is determined by its programmatic control. Consequently, a clunky UI with a strong API like Salesforce can be superior for AI integration than a tool with a slick UI but a weak API.

Software abstractions (e.g., cross-platform frameworks) make it easy to build a baseline product, raising the floor of quality. However, they often prevent you from reaching world-class status by limiting access to native capabilities, thus lowering the ceiling.

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.

Linear is pivoting its core value proposition, arguing that traditional issue tracking is obsolete when an AI agent can fix a bug in minutes while the human approval process takes a week. Linear now aims to be the essential context layer that directs AI agents, shifting from managing tasks to orchestrating AI work.

The future value in code management isn't just storing files; it's owning the layer that understands how code connects across services. This operational domain is where AI agents function, signaling an inevitable category shift that companies like OpenAI are already exploring internally.

Even before AI, Linear moved away from the "software factory" model where PMs decide, designers draw, and engineers code. They empower the builders (designers and engineers) to make critical decisions during execution. This prevents bad ideas from being implemented just because they were "approved" and improves overall product quality.

To serve both solo developers and large enterprises, GitHub focuses on creating horizontal "primitives" and APIs first. This foundational layer allows different user types to build their own specific workflows on top, avoiding the trap of creating a one-size-fits-none user experience.

While Linear started by creating a platform for third-party agents, they found they couldn't control or improve the end-to-end user experience. This limitation prompted them to build their own coding agent to create a smoother, more integrated workflow where context is automatically injected.

Using a composable, 'plug and play' architecture allows teams to build specialized AI agents faster and with less overhead than integrating a monolithic third-party tool. This approach enables the creation of lightweight, tailored solutions for niche use cases without the complexity of external API integrations, containing the entire workflow within one platform.