DoorDash's AI strategy is evolving from simple chatbots to true agentic commerce. This means the system won't just suggest food but will take action, such as automatically ordering a user's lunch by integrating with their calendar to know when they're available, creating a fully automated, personalized experience.

An API is no longer enough; it must be optimized for AI agents. This means enabling high-volume calls and structured outputs that AI can easily consume. New agentic products will be built on the most accommodating platforms, leaving others behind.

Listen Labs envisions its platform evolving into a "Human API." This would allow other AI agents—like a coding agent—to programmatically query human preferences before building a feature. It helps them know "what to build" by accessing up-to-date insights from a target user base, connecting strategy to execution.

For companies building AI agents, the key indicator of a successful customer engagement is the availability of well-documented APIs. These APIs are essential for the agent to take action and look up data, which directly enables a superior, elevated experience from day one.

An agent's power comes from its deep context about a user's business and life. Maintaining a detailed, structured personal knowledge base in a tool like Obsidian, which can be fed to the agent, is the most critical step to creating an agent that feels like a "second brain" and can operate with genuine understanding.

DoorDash recognizes that web traffic is now majority non-human agents. Their strategy is shifting to be "agentic first," creating interfaces like a CLI that allow other AI agents to programmatically use their service, unlocking new use cases like automated pantry restocking.

Frame your personal and professional information as a structured set of machine-readable files. This "operating manual" allows AI agents to understand your roles, goals, and constraints without constant re-explanation, just as a developer uses API docs to interact with software.

The most powerful applications for personal AI agents go beyond simple task automation. They involve managing and analyzing overwhelming personal data streams, such as tracking health inputs to diagnose issues or filtering the signal from the noise of constant notifications.

Generic AI tools provide generic results. To make an AI agent truly useful, actively customize it by feeding it your personal information, customer data, and writing style. This training transforms it from a simple tool into a powerful, personalized assistant that understands your specific context and needs.