Mastering Cursor AI Agents: Best Practices for Efficient Code Generation

Understanding the Agent Harness

An agent harness consists of three core components: Instructions (system prompts and rules), Tools (file editing, repository search, terminal execution, etc.), and User messages (the prompts you give the agent). Cursor assembles these components for each supported model and tailors them based on internal evaluations and external benchmarks.

Start with Planning

Before writing code, plan your task. Research from the University of Chicago shows experienced developers prefer planning, which clarifies goals for the agent.

Plan Mode

Press Shift+Tab in the agent input box to enable Plan mode. The agent will then:

Analyze the repository to locate relevant files.

Ask clarification questions about your requirements.

Generate a detailed implementation plan with file paths and code references.

Wait for your confirmation before starting any changes.

The plan opens as a Markdown file that you can edit to remove unnecessary steps, adjust the approach, or add missing context.

When to Restart a Conversation

Start a new conversation if you switch tasks, the agent appears confused, or you have completed a logical work unit. Continue the current conversation when you are iterating on the same feature, need prior context, or are debugging recent changes.

Managing Context

Provide each agent with the context it needs. Let the agent fetch files automatically using tools like grep or semantic search. If you know the exact file, reference it directly; otherwise, let the agent locate it.

Use @Branch to give the agent the current Git branch context, enabling queries such as “Review the changes on this branch.”

Extending the Agent

Cursor supports two ways to customize agent behavior: Rules (static context applied to every conversation) and Skills (dynamic capabilities invoked on demand).

Rules: Static Context

Create Markdown files under .cursor/rules/ to define persistent commands, coding style, and workflow guidelines. Example rule file:

# Commands
- `npm run build`: Build the project
- `npm run typecheck`: Run the type‑checker
- `npm run test`: Run tests (prefer single test files for speed)

# Code Style
- Use ES modules (import/export) instead of CommonJS
- Prefer destructured imports, e.g., `import { foo } from 'bar'`
- Refer to `components/Button.tsx` for standard component structure

# Workflow
- Always type‑check after a series of code changes
- API routes go in `app/api/` following existing patterns

Avoid copying entire style guides, listing every possible command, or adding rules for rare edge cases.

Skills: Dynamic Capabilities

Define Skills in a SKILL.md file. They can include:

Custom commands : reusable workflows triggered with /.

Hooks : scripts that run before or after an agent action.

Domain knowledge : task‑specific instructions the agent can call on demand.

Skills are loaded only when the agent deems them relevant, keeping the context window small while providing specialized abilities.

Example: Long‑Running Agent Loop

Use a Skill to create a hook that runs until all tests pass. First, configure the hook in .cursor/hooks.json:

{
  "version": 1,
  "hooks": {
    "stop": [{
      "command": "bun run .cursor/hooks/grind.ts"
    }]
  }
}

Then implement the hook script ( .cursor/hooks/grind.ts) that reads input from stdin and returns a followup_message to continue the loop:

import { readFileSync, existsSync } from "fs";

interface StopHookInput {
  conversation_id: string;
  status: "completed" | "aborted" | "error";
  loop_count: number;
}

const input: StopHookInput = await Bun.stdin.json();
const MAX_ITERATIONS = 5;

if (input.status !== "completed" || input.loop_count >= MAX_ITERATIONS) {
  console.log(JSON.stringify({}));
  process.exit(0);
}

const scratchpad = existsSync(".cursor/scratchpad.md")
  ? readFileSync(".cursor/scratchpad.md", "utf-8")
  : "";

if (scratchpad.includes("DONE")) {
  console.log(JSON.stringify({}));
} else {
  console.log(JSON.stringify({
    followup_message: `[Iteration ${input.loop_count + 1}/${MAX_ITERATIONS}] Continue working. Update .cursor/scratchpad.md to DONE when finished.`
  }));
}

This pattern works for any goal‑oriented task where success can be verified, such as running tests until they all pass or iterating UI until it matches a design.

Handling Images

Agents can process images supplied in prompts—screenshots, design files, or image paths—allowing visual debugging and design‑to‑code conversion.

Common Workflows

Test‑Driven Development (TDD)

Ask the agent to write tests based on expected input/output.

Run the tests and confirm they fail (no implementation yet).

Submit the failing tests.

Instruct the agent to write code that passes the tests without modifying them.

Submit the implementation once the tests succeed.

Understanding a New Codebase

Ask targeted questions such as “How does the logging system work?” or “What does CustomerOnboardingFlow handle?” The agent uses grep and semantic search to locate relevant code.

Git Workflow Automation

Define reusable commands in .cursor/commands/ and invoke them with a leading slash. Example /pr command creates a pull request:

1. Use `git diff` to view staged and unstaged changes.
2. Write a clear commit message.
3. Commit and push to the current branch.
4. Run `gh pr create` to open a PR with title and description.
5. Return the PR URL.

Other examples include /fix-issue [number] (uses gh issue view) and /update-deps (updates dependencies one by one, testing after each).

Code Review

During generation, the diff view updates in real time; press Escape to abort and re‑guide the agent. After generation, click Review → Find Issues to run an automated review that flags potential problems.

Parallel Agent Execution

Cursor can run multiple agents in parallel using isolated Git worktrees, preventing interference. Choose the “worktree” option, then click Apply to merge changes back into your branch.

You can also run the same prompt on several models simultaneously, compare results side‑by‑side, and let Cursor suggest the best solution. This is useful for tricky problems, model‑family quality comparison, and uncovering edge‑case gaps.

Delegating Tasks to Cloud Agents

Cloud agents clone your repository, create a branch, work autonomously, and open a pull request when finished. You receive notifications via Slack, email, or the web UI, then review and merge the changes.

Debug Mode for Stubborn Bugs

When standard interaction fails, Debug Mode generates multiple hypotheses, adds logging statements, asks you to reproduce the bug while collecting runtime data, analyzes the behavior, and proposes evidence‑based fixes. It is ideal for reproducible bugs, race conditions, performance issues, and regressions.

Building an Effective Workflow

Successful developers share these habits:

Write specific prompts (e.g., “Write a test case for auth.ts covering the logout edge case”).

Iteratively refine rules and commands only after the agent repeatedly makes the same mistake.

Perform thorough code reviews; AI‑generated code can contain subtle errors.

Provide verifiable goals—strong typing, linters, and tests give the agent clear success signals.

Treat the agent as a capable collaborator: request plans, ask for explanations, and challenge unsatisfactory solutions.

These practices help you get the most out of programming agents as they continue to evolve.