Summary & Key Takeaways ​

• Identifies three key roles of context for AI agents: global, local, and ambient.
• Global context applies across multiple tasks, providing overarching knowledge.
• Local context is specific to a single task, offering immediate relevance.
• Ambient context refers to raw data streams, such as emails or notifications.
• Power users actively curate these different contexts to enhance agent performance.
• Understanding these roles is crucial for designing more effective and intelligent AI products.

Our Commentary ​

This is a really insightful breakdown of context for AI agents. We often talk about "context windows," but NNGroup's distinction between global, local, and ambient context is a much more nuanced and practical way to think about how agents operate. It's a good framework for anyone trying to build or even just understand more sophisticated AI systems.