Mastering Cursor AI Agents for Production-Ready Software

Start a new chat for every discrete task to keep the agent's working memory clean.

Lee runs ~20 separate agent conversations per day, each handling 30-75 line changes. He explicitly tags files to pull them into context instead of letting the agent guess.

Why: Long context windows degrade quality; models get confused when stuffed with 500k tokens. Early research shows measurable drop-off in output quality as context grows.

"for each new task, you get the best quality out of working with the model, you can just start new chats for each thing that you want to do"

Set up automated guardrails so agents can self-correct without human intervention.

Use TypeScript, linters, formatters, and tests. The agent reads its own outputs and fixes linter errors automatically.

Why: Reduces human review cycles and ensures production-ready code from the first pass.

"I've taken some measures ahead of time to make sure that the agent can basically fix its own outputs"

Write prompts like you're briefing a competent intern—direct but not verbose.

Lee's prompts are short: 'add event tracking when the Add to Cursor button for an MCP was clicked' or 'reset the filter input when navigating items'.

Why: Over-detailed prompts waste context tokens and can confuse the model with unnecessary constraints.

"it's not really that complex of a prompt. I just said very directly, here's what you need to do"

Build custom review agents that encode your team's specific gotchas and standards.

Lee maintains a markdown file with banned words (mission critical, seamless) and code review checks (loading states, auth changes, caching opportunities).

Why: Automates tribal knowledge transfer and catches issues that generic linters miss.

"building these own customized commands and customized props for your use case has been very helpful for me"

Run Cursor agents headlessly in CI/CD to maintain zero-bug policies.

Use the CLI in GitHub Actions to auto-fix failing tests, update docs on PR merge, or audit security vulnerabilities.

Why: Turns bug fixes from hours-long context switches into Slack commands that run while you're AFK.

"they have a zero bug policy and they use Cursor... the engineer who's triaging just tells Cursor, hey, investigate this and fix it"

You don't need to be a developer, but you do need to peek under the hood eventually.

There's a spectrum from never-look-at-code to artisanal hand-coding. Both coexist, but even 'non-devs' benefit from understanding the source of truth.

Why: Abstractions leak; knowing the code gives you superpowers to fix edge cases that no-code tools can't handle.

"you kind of reach a point where eventually you kind of have to look at the code and it's going to make your life a lot easier"

When code is free to generate, build disposable tools that solve one specific problem.

Examples: custom debugging HUDs, A/B migration visualizers, throwaway analytics dashboards. Previously too expensive to build for single use.

Why: Zero marginal cost of code changes the economics of software—you can afford to build tools you'll use once.

"when writing code isn't the hard part, it's like, well, heck yeah, I want a visual GUI to watch this migration"

Use viral personal software as lead magnets to bootstrap distribution for your real product.

Build public, shareable tools that demonstrate your capabilities while capturing an audience for your core offering.

Why: As products become easier to build, distribution becomes the moat. Personal software can manufacture that distribution.

"I've been calling it a distribution engineer... it's not necessarily about getting followers. It's about getting distribution"