AI Agents

Agents

The “agent” is software that calls an LLM with your prompt and passes it a set of tool definitions, then calls any tools that the LLM requests and feeds the results back into the LLM.

Good code & Bad code

Code is good if all apply:

• The code works. It does what it’s meant to do, without bugs;
• We know the code works. We’ve taken steps to confirm to ourselves and to others that the code is fit for purpose;
• It solves the right problem;
• It handles error cases gracefully and predictably: it doesn’t just consider the happy path. Errors should provide enough information to help future maintainers understand what went wrong;
• It’s simple and minimal - it does only what’s needed, in a way that both humans and machines can understand now and maintain in the future;
• It’s protected by tests. The tests show that it works now and act as a regression suite to avoid it quietly breaking in the future;
• It’s documented at an appropriate level, and that documentation reflects the current state of the system - if the code changes an existing behavior the existing documentation needs to be updated to match;
• The design affords future changes. It’s important to maintain YAGNI - code with added complexity to anticipate future changes that may never come is often bad code - but it’s also important not to write code that makes future changes much harder than they should be;
• All of the other relevant “ilities” - accessibility, testability, reliability, security, maintainability, observability, scalability, usability - the non-functional quality measures that are appropriate for the particular class of software being developed.

KBs

Key asset to develop as a software professional is a deep collection of answers to questions about software, accompanied by proof of those answers. Hoard solutions in blogs on things you’ve figured out how to do. Write diffrent projects, small proof-of-concepts that demonstrate a key idea. Examples:

• https://simonwillison.net/
• https://til.simonwillison.net/
• https://github.com/simonw
• https://tools.simonwillison.net/
• https://simonwillison.net/2025/Dec/10/html-tools/
• https://github.com/simonw/research Aside from helping you build and extend your own abilities, the assets you generate along the way become powerful inputs for your coding agents. A cool way to create prompting patterns is to tell an agent to build something new by combining two or more existing working examples.

Compound engineering loop

https://every.to/chain-of-thought/compound-engineering-how-every-codes-with-agents Every coding project completed ends with a retrospective, called the compound step, where what worked is taken and documented for future agent runs.

If we want the best results from our agents, we should aim to continually increase the quality of our codebase over time. Small improvements compound. Quality enhancements that used to be time-consuming have now dropped in cost to the point that there’s no excuse not to invest in quality at the same time as shipping new features. Coding agents mean we can finally have both.