An agent harness is the basic structure that controls how an agent works. It consists of three parts: instructions that give the agent rules of behavior; tools, such as editing files, searching code, or running commands in the terminal; and your messages, which tell the agent what to do. In Cursor, this harness is customized for different models, such as those from OpenAI and others, based on internal testing. For example, some models prefer using the grep command for searching, while others need extra instructions to check the code after making changes. This means you do not have to worry about the details—just focus on your project.
Start with a plan to keep everything running smoothly
One of the most important pieces of advice is to plan before the agent starts writing code. A study from the University of Chicago found that experienced developers plan more often, which helps clearly define goals. In Cursor, press Shift+Tab to enter Plan Mode. The agent will then explore your code, ask clarifying questions, create a detailed plan with file paths, and wait for your approval. Plans are saved as Markdown files that you can edit and even store in the .cursor/plans/ folder for team documentation. If the agent gets something wrong, return to the plan, revise it, and run it again—it is faster than fixing errors one by one.
How to properly manage context for the agent
Your role shifts to providing the right context so the agent knows what to do. You do not need to manually tag every file—the agent has tools such as semantic search or grep that can find the relevant parts of the code on their own. For example, if you say, "look at the authentication flow," the agent will search it by itself. Use tools such as @Branch for context about the current branch. If the conversation goes on for a long time and the agent loses track, start a new one—this is better when changing tasks or when the same errors keep appearing. To refer to previous work, use @Past Chats, which is more efficient than copying entire conversations.
Extend the agent with rules and skills
Cursor allows you to customize the agent with rules and skills. Rules are static context in a RULE.md file in the .cursor/rules/ folder, where you describe commands such as npm run build, coding styles, or workflows. Keep them short and link to examples in the code rather than copying entire guides. Skills are dynamic, defined in SKILL.md, and include custom commands, automation hooks, or domain knowledge. For example, a hook in .cursor/hooks.json can make the agent repeat a task until all tests pass, with a limit of 5 iterations. Skills are available in the nightly version of Cursor.
Working with images and common workflows
The agent can handle images directly in the prompt—insert a screenshot of a design and have it implement the layout, colors, or spacing. To debug errors, send a screenshot of the error state. Common workflows include test-driven development: the agent writes tests, runs them to make sure they fail, then implements the code until they pass. To understand the code, ask questions as you would a colleague: "How does logging work?" The agent searches the git history, resolves conflicts, or automates commands such as /pr to create a pull request.
Running agents in parallel and cloud options
Cursor supports parallel agents in separate worktrees, where you can select multiple models and compare the results. For cloud agents that run remotely, describe the task, and the agent will clone the repo, create a branch, and open a pull request. Monitor it from the web or your mobile device. For complex bugs, use Debug Mode, which generates hypotheses, adds logging, and analyzes data from reproducing the bug.
Code review and developing your own style
Monitor the agent as it works in the diff view and interrupt it with Esc if it goes off track. Once it is finished, run Review → Find Issues to check the code. Bugbot is available for pull requests. Generate Mermaid diagrams for architecture, such as the data flow in authentication. The key is to write specific prompts, iterate on the setup, review carefully, and give the agent clear goals such as tests.
