Designing agentic loops
Coding agents like Anthropic’s Claude Code and OpenAI’s Codex CLI represent a genuine step change in how useful LLMs can be for producing working code. These agents can now directly exercise the code they are writing, correct errors, dig through existing implementation details, and even run experiments to find effective code solutions to problems.
As is so often the case with modern AI, there is a great deal of depth involved in unlocking the full potential of these new tools.
A critical new skill to develop is designing agentic loops.
One way to think about coding agents is that they are brute force tools for finding solutions to coding problems. If you can reduce your problem to a clear goal and a set of tools that can iterate towards that goal a coding agent can often brute force its way to an effective solution.
My preferred definition of an LLM agent is something that runs tools in a loop to achieve a goal. The art of using them well is to carefully design the tools and loop for them to use.
The joy of YOLO mode
Agents are inherently dangerous—they can make poor decisions or fall victim to malicious prompt injection attacks, either of which can result in harmful results from tool calls. Since the most powerful coding agent tool is “run this command in the shell” a rogue agent can do anything that you could do by running a command yourself.
To quote Solomon Hykes:
An AI agent is an LLM wrecking its environment in a loop.
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