To prevent an AI agent from repeating mistakes across coding sessions, create 'agents.md' files in your codebase. These act as a persistent memory, providing context and instructions specific to a folder or the entire repo. The agent reads these files before working, allowing it to learn from past iterations and improve over time.

By combining modular prompts for models (gender, age, body type) with image-to-text descriptions of clothing, you can create automated workflows. These systems generate entire photoshoots, including 360-degree views and action shots, solving the problem of photographing seasonal inventory at scale.

To fully leverage memory-persistent AI agents, treat the initial setup like an employee onboarding. Provide extensive context about your business goals, projects, skills, and even personal interests. This rich, upfront data load is the foundation for the AI's proactive and personalized assistance.

To create detailed context files about your business or personal preferences, instruct your AI to act as an interviewer. By answering its questions, you provide the raw material for the AI to then synthesize and structure into a permanent, reusable context file without writing it yourself.

Don't try to create a comprehensive "memory" for your AI in one sitting. Instead, adopt a simple rule: whenever you find yourself explaining context to the AI, stop and immediately have it capture that information in a permanent context file. This makes personalization far more manageable.

Don't limit an AI agent to tasks you can already imagine. After providing full context on your work, ask it open-ended questions like, “How can you make my life easier?” This strategy of “hunting the unknown unknowns” allows the AI to suggest novel, high-value workflows you wouldn't have thought to request.

Bridge the physical-digital divide in family scheduling. Take a picture of a physical wall calendar and feed it to an AI agent like Claude. Using MCPs for Google Calendar, the agent can parse the image and automatically create or update digital events, even adding buffer time for travel.

Building a "second brain" often fails due to tedious manual data entry. Bypass this by using an AI agent's multimodal capabilities. Simply take photos of activities or book pages. The agent can then parse these images and automatically log the relevant information into a structured format (e.g., a homeschool lesson log in Obsidian), eliminating friction.

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.