julin

Programming is becoming less important. Code review is becoming less important too. Coding agents can write code, find bugs, run tests, and change large codebases. Work that took days can now take minutes. But something strange is happening. Agents can write most of the code, yet people still spend much of their time deciding what the agents should do. A person gives an agent a task, examines the result, and decides what to do next. The agent can complete many steps without help. But when a decision can change the direction of a project, the person often takes control again.

I’ve been working on Rio, a coding agent designed to run without a human continuously steering it. A task may take minutes, hours, or days. The agent needs to keep making progress even when nobody is watching. The first versions of Rio used Jupyter notebooks as both context and execution environment. The model could write and edit cells, execute them, and use their results to decide what to do next. This worked surprisingly well. A notebook is already a structured, executable document. It has cells, outputs, and a format that can be saved to disk. There is no need to invent a new programming language or an elaborate workflow graph.

AI coding is powerful. It is also risky if you are not cautious. The AI does the work. You are responsible for everything. The bottom-line skill for every engineer nowadays is the judgment to recognize when the AI is performing risky operations. ## Know What You’re Exposing Coding sessions can be stored remotely. LLM requests can be routed through proxies. It’s a good practice to limit what the agent and the LLM model can see.

# Code Mode: Moving Agent Control Flow Out of the LLM Most agents still use tools one call at a time. The model decides to call a tool. The runtime executes it. The result is appended to the conversation. The model runs again and decides what to do next. For a short task this works well. For a task that needs twenty API calls, filtering, retries, a loop, or several dependent operations, the design becomes expensive:

I lied. This article doesn’t show you 100 ways to extend AI agents. But there are definitely more ways than most people realize. Here are the ones I’ve discovered. ## 1. AGENTS.md Harnesses auto-load this file when a session starts. It’s where you can change agent behavior by writing markdown documentation. Claude Code previously only supported CLAUDE.md, but they’ve recently added AGENTS.md support. (Thanks Shopify CEO Tobi.) ## 2. Skills The LLM receives the name and description from skill frontmatter in your installed SKILL.md files—either globally or in the project directory (.agents/skills, ./skills). The LLM progressively requests full skill content as needed, revealing more of the skill in the prompt dynamically.