Grok Bot engineer Lauren Tan shares a checklist for trusting agents' work, ranking better code first and human review last
how do you trust your agents' work more? copy this post/link and give it to your @bot or agent! 1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again. 2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time. 3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill. 4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default. this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night. install t.co/u9xIlN3eN0 and create a new Project: 1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture 2. use /recall on my past transcripts to learn how i steer agents 3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data. 4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me
Context
As agents write more code than people can review, advice on building codebases that keep agents on track is becoming central to how teams work.
This is a first hand opinion from Lauren Tan, who works on Grok Bot at SpaceXSI and previously worked on the React compiler. The post also promotes her own set of agent skills.
Deep dive
On October 16, Lauren Tan, who posts as poteto, asked how people can trust their agents' work more, and told readers to copy the post and give it to their Grok Bot or agent.
She listed four layers. First, codebase improvements: better architecture, stronger types and clear boundaries, because agents copy what exists and take the shortest path, so the easy thing should be the right thing. Second, static analysis such as lint rules, tests, continuous integration and formal verification, which she called very token efficient. Third, prose such as comments, rules, AGENTS.md files and skills, for things that need agent judgment, used minimally. Fourth, human review, which she called the hardest to scale.
The attached drawing shows these layers as an inverted pyramid under the title whenever you correct your agent, meaning most effort should go to the codebase at the top and least to human review at the bottom.
She ended with a prompt for installing her skills and asking an agent to study her past feedback and commits, find the most common pitfalls agents fall into, and propose a plan to make her code more agent friendly.
What to expect next
Watch whether teams adopt this kind of layered approach, and whether agent makers build features that turn repeated corrections into lint rules or tests automatically.
Also watch how Grok Bot and rival agents such as Claude Code and Codex handle project rules and skills, since Tan's advice depends on agents reading them. Shared standards for files like AGENTS.md could make this advice easier to apply across tools.