#AI Agents
7 articles exploring AI Agents. Expert insights and analysis from our editorial team.
Articles
Hugging Face Skills: Pretrained Agent Capabilities
Hugging Face Skills are standardized, self-contained instruction packages that give coding agents—Claude Code, Codex, Gemini CLI, and Cursor—procedural expertise for AI/ML tasks. Launched in November 2025, the Apache 2.0-licensed library reached 7,500 GitHub stars by early 2026 and provides nine composable capabilities from model training to paper publishing.
Superpowers: The Agentic Framework Replacing Your Dev Process
Superpowers is an open-source agentic skills framework by Jesse Vincent that enforces structured software development workflows—brainstorming, planning, TDD, and subagent coordination—on top of AI coding agents like Claude Code, turning them from reactive assistants into disciplined developers capable of autonomous multi-hour sessions.
How AI Agents Remember: Memory Architectures That Work
AI agents use four distinct memory tiers—working, episodic, semantic, and procedural—stored across context windows, vector databases, knowledge graphs, and model weights. Choosing the right architecture determines whether your agent stays coherent across sessions or forgets everything the moment a conversation ends.
Browser-Use Agents: AI That Browses Like a Human
A comprehensive guide to browser-use AI agents, exploring OpenAI Operator, Claude Computer Use, Browser-Use framework, and Google Project Mariner with benchmarks and capabilities.
How Much Autonomy Should AI Agents Have? A Framework for Trust
As AI agents gain real-world capabilities—browsing, coding, purchasing—the question of how much autonomy to grant these systems becomes critical. This article proposes the VERIFIED framework for determining appropriate trust levels.
Memory: The Missing Piece in AI Agents
Why memory is the critical bottleneck in AI agent architecture, how RAG and vector databases solve part of the problem, and where the field is heading next.
Pydantic AI vs LangChain: A Developer's Guide to the New Generation of Agent Frameworks
A comprehensive comparison of Pydantic AI and LangChain, exploring type safety, developer experience, and production readiness in modern Python AI agent frameworks.