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This paper presents design principles for generative AI systems with a focus on environmental sustainability and a 'slow' approach to development and deployment.
The article proposes that AI systems should intentionally incorporate non-response states, inspired by musical concepts like tacet and hold, to preserve uncertainty and avoid premature resolution in outputs.
A design principle for AI agent permission systems: gate actions by reversibility rather than risk, with an autonomy ladder, reversibility tags, and unconditional forcing functions for irreversible actions.
The author outlines eight design principles for transforming a traditional company into an AI-native organization, covering reliable context, queryable systems, feedback loops, earned autonomy, and security, emphasizing that this is an organizational transformation as much as an engineering one.
The author promotes his open-source book 'In-depth Understanding of AI Agents: Design Principles and Engineering Practices', continuously revised and incorporating the latest research results, covering Agent design principles and engineering practices, with example code and experiments.
This 1996 paper by Gentner and Nielsen explores alternative user interfaces by reversing the core design principles of the Macintosh, leading to a language-based, expert-user-oriented interface.
This paper compares socio-technical design principles with guidelines for human-centered AI, analyzing their similarities and differences to inform future AI design approaches.
A curated set of 17 design and motion principles distilled from Apple's WWDC videos, packaged as a reusable skill for design engineers to review and improve UI animations and interfaces.
Solomon Neas rewired his systems (Brigade, Graphtrail, MiseLedger) using design principles from Active Graph papers, adopting the habit that 'state is a projection of the log', which yoheinakajima praises as a great walkthrough for agents.
12-Factor Agents is an open-source project that condenses the core design principles for building production-grade AI Agent applications into 12 actionable engineering methodologies, covering key aspects like context management, tool calling, state modeling, etc., and has gained 11k+ GitHub stars.
Matt cites John Ousterhout's view, pointing out that AI is better at refactoring 'deep modules' with simple interfaces but rich functionality, while struggling with 'shallow modules' that have complex interfaces but single-purpose functionality.
The OpenCLI project proposes the Agent-native design concept, making the AI agent the CLI's primary user, with all capability design measured by its improvement to agent success rates.