Instead of giving an AI agent general access to a tool's full API, build a specific adapter. This intermediary layer exposes only the necessary functions for a given task, preventing the agent from 'wandering' through traces or using APIs inefficiently. This makes tool integration more precise and reliable.

Instead of using one large model for all tasks, Linear employs a small router model. For high-frequency use cases like creating issues, it routes the request to a smaller, highly-optimized model and prompt, saving costs while improving performance and reliability.

The key product innovation of Agent Skills is changing the user's perception of AI. Instead of just a tool that answers questions, AI becomes a practical executor of defined workflows, making it feel less like a chat interface and more like powerful, responsive software.

Don't write agent skills from scratch. First, manually guide the agent through a workflow step-by-step. After a successful run, instruct the agent to review that conversation history and generate the skill from it. This provides the crucial context of what a successful outcome looks like.

The "Agent Skills" format was created by Anthropic to solve a key performance bottleneck. As capabilities were added, system prompts became too large, degrading speed and reliability. Skills use "progressive disclosure," loading only relevant information as needed, which preserves the context window for the task at hand.

A key capability of advanced AI agents is their ability to read API documentation and write the necessary code ("skills") to integrate with new services on the fly. This turns every tool with an API into a potential native integration, dramatically expanding the agent's capabilities without manual developer work.

Simply giving an AI agent thousands of tools is counterproductive. The real value lies in an 'agentic tool execution layer' that provides just-in-time discovery and managed execution to prevent the agent from getting overwhelmed by its options.

A generic API lets users build their own processes, which can be inconsistent. By building a native agent, a company like Linear can embed its specific philosophy and best practices (e.g., "the Linear method") into the agent's core behavior, ensuring quality.

Standard APIs for human developers are often too verbose for AI agents. Notion created agent-centric APIs, like a special markdown dialect and a SQLite interface, by treating the AI as a new type of user. This involved empirical testing to understand what formats agents are naturally good at using.