AI Agents

Agent teams let you break work into focused tasks where each teammate gets a fresh context window dedicated to its piece of the problem. This guide walks through the full setup on macOS, from enabling the feature flag to managing your first multi-agent session with tmux.

Your team built an AI agent. It works in a demo. Now someone asks: where does it actually run? A new category of product has emerged to solve this: agent platforms. Think of them as fleet management software for AI agents. This post compares the major options and how to choose.

Token Guard is a GitHub Action that counts tokens in your agent context files and enforces limits in CI. Here’s why we check agent context into our repos, and why keeping it lean matters for team collaboration.

Most developers try Claude Code and form an opinion based on basic usage. But the engineers getting the most out of it treat it as a full development environment. Here are the advanced techniques that separate power users from everyone else.

There are at least a dozen frameworks competing to be the foundation for your agent architecture. This post breaks down how each one thinks about the problem, where it shines, and where it will cost you time. We are not ranking them. The right choice depends on your use case.

The team that brought type safety to Python web development with Pydantic has just unveiled their take on AI development: Pydantic.ai. This new framework reimagines how we build AI applications by bringing Pydantic’s legendary validation capabilities to the world of Large Language Models.