When you're not sure what you want yet. Ask the agent to fan out across several directions and lay them next to each other so you can point at one — instead of reading three sequential walls of text and trying to hold them all in your head. And once you've picked, turn the pick into a plan the implementer can actually read.
On Programming with Agents
From the Zed Blog: Agents handle typing so we can focus on thinking.
Workshop: "AI Agents & Web Browsers" · Issue #507 · w3c/strategy
NOTE: This is a tentative workshop draft — Dom and Roy are working on a CFP: Introduction This W3C Workshop aims to gather stakeholders to explore how AI agents can interact with and be integrated ...
Agents Done Right: A Framework Vision for 2026
Agents choke on context, loop on failures, and dump walls of code for review. It's time to rethink the architecture.
Peter Steinberger 🦞 on Twitter / X
Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore.You should be designing loops that prompt your agents.— Peter Steinberger 🦞 (@steipete) June 7, 2026
Joshua Gu on Twitter / X
Recent agentic systems (Claude Code, Codex, RLM, etc.) push context out of the prompt and into the environment (e.g., as files). This helps them maintain long-term knowledge about their goals and functionality.🚨 While this is a good idea, we show a surprising result: systems… pic.twitter.com/XHFVaDcr4l— Joshua Gu (@astrogu_) May 20, 2026
Agent Design Is Still Hard
My Agent abstractions keep breaking somewhere I don’t expect.
Agent Skills
AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...
Tiles Notebook | Note Taking Tool With AI Agents
A notebook that makes working with AI agents easier.
Agentic Engineering Patterns - Simon Willison's Weblog
Patterns for getting the best results out of coding agents like Claude Code and OpenAI Codex. See my introduction for more on this project.
evalstate/fast-agent
Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP Support
Writing effective tools for AI agents—using AI agents
Writing effective tools for AI agents—using AI agents
Popmelt — Design copilots for AI agents
Our Taste models encode design expertise and philosophy, giving AI agents instant access to a curated catalog of aesthetic knowledge. Unlike traditional templates and design systems, our imprints are AI-native and guided by universal principles. This means agents can apply them to any interface scenario, not just predefined layouts and elements. They're also customizable and extensible, letting you build quickly without falling into the cookie-cutter UI library trap.
Deep Agents
Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are “shallow” and fail to plan and act over longer, more complex tasks. Applications like “Deep Research”, “Manus”, and “Claude Code” have gotten around this limitation by
