Coding With AI That Survives Review

Give the agent one named task, only the context it needs and a check you can rerun. A task shaped this way produces a diff small enough to review and a result you can repeat.

Each move fails in its own way. Without a boundary, the agent edits files you never meant to open, and the diff becomes hard to read. Thin context fails more quietly: the code compiles and the tests pass, but the change ignores patterns the rest of the repo follows. Without a check you can rerun, you are taking the agent's word that the work is done.

Asking for a plan before the agent writes code is the easiest step to skip. It means a short wait while nothing visible happens, and the price of skipping it shows up later, in review.

If the plan names files you didn't expect, you learn that before any code exists. If it names the right files, you have a list to check the diff against. Once four hundred lines of diff are on screen, changing the approach means throwing that work away.

The habit that matters most for a team is leaving a trail the next reviewer can follow. The steps below keep the prompt and its proof attached to the change.

1. 1Pick one real backlog item with a clear owner and expected result.
2. 2Add only the context the agent needs: files, failing output, constraints and done state.
3. 3Ask for a plan before code when the task touches more than one file.
4. 4Run checks that match the risk: unit test, typecheck, visual pass or review checklist.
5. 5Capture the prompt, diff, result and reviewer note so the workflow can be repeated.

Match the effort to the change. A one-file fix needs a clear done state and a typecheck, and a plan adds little. A change across several files is where a plan pays for itself, and where the check should include tests and a reviewer who knows that code.

Keep whatever lets someone rerun the work or pick it up later. A finished task is the merged code plus a short trail that shows how it got there.

• The prompt or plan that shaped the work.
• The files changed and the reason each file changed.
• The command, screenshot or review note that proved the result.
• The rule, checklist or template you would reuse next time.

The last item pays off over time. A rule or template saved from one good run means the next run starts from what already worked, not from a blank prompt.

Cursor ships often, so advice about it can go out of date. The table summarizes Cursor's recent releases, so you can check this page against the current product. For each release's date and sources, see What's new in Cursor 2026.

Yes. The loop that turns an issue into a reviewable diff can also turn a fuzzy knowledge-work problem into a working tool. The steps are the same: name the problem, dump in the context, iterate on the approach, test on a few examples, then refine.

What changes is who does which part. You remain the domain expert, the person who knows what good output looks like and where the edge cases hide. The agent does the repetitive execution. You stay in the loop because you are the one who can tell whether a result is right.

Pick the surface by how you will check the result. If you can judge the work from what the agent hands back, send it to a cloud agent. If you expect to redirect the agent partway through, keep it local, where you can watch the run and stop it. Cloud agents need Premium or higher, a Teams or Enterprise plan, or a legacy paid plan, so on Free or Plus every run is local.

Timing makes the split concrete. A presenter in one Cursor webinar put a cloud run at 10 to 15 minutes end to end at minimum, each run on its own branch, and Cursor's docs set no limit on how many run in parallel. That is too slow for a two-minute edit and a good fit for work you can hand off and come back to. So the deciding question is whether the task can run for those 15 minutes without your attention.

Automations sit one step further out. An automation is a cloud agent with a trigger, an instruction and a deliberately narrow set of tools. Don't build one for a workflow nobody has run by hand. Run it manually a few times first, so you know what good output looks like before a schedule starts producing it every morning. A bad automation fails quietly: it keeps producing output that nobody reads.

The SDK sits furthest out. Use it when the caller is your own code rather than a person. The SDK agents guide covers which key a run should carry and whose budget it spends.

They break in the context window far more often than in the model choice. Presenters in Cursor's webinars describe output quality dropping steeply as the window fills: noticeably by 70 to 80% full, and definitely before 90%. Their advice settles on a working ceiling of about 60 to 65%, and several of them start a new chat at about 50%.

Compaction is why running to the limit is a bad trade. Near the limit, the agent compresses what came before and then behaves as if it never had the detail. You still pay for all of those tokens.

A bigger window doesn't fix this. Before you get close, ask the agent for a summary of what it has done, and paste that summary into a fresh agent. The new agent doesn't need the old conversation. It needs the codebase, the commits and the code the last agent wrote, and all of that stays in the repo when the chat ends.

Switching models mid-session has a cost of its own. It breaks the cache, so an unplanned swap loses the cached context as well as time. Where you can, choose the model before you start.

Measurement becomes the hard part. A University of Chicago study of tens of thousands of Cursor users found that companies merged 39% more pull requests after Agent became Cursor's default mode. Merged pull requests are easy to count, and on their own they show nothing about whether the changes held up.

Read velocity next to quality. On its own, a velocity number rewards code that gets rewritten the following week. The pair to watch is time to market against the trend in customer-facing defects. If you can watch only one thing, check whether features reach customers faster while defects stay flat.

Two common dashboard metrics predict little on their own. A high count of AI-written lines mostly means more code to review and maintain. An active-user count treats someone who opens the agent once a week the same as someone who rebuilt their week around it.

Start with Cursor's own instrumentation before you build a dashboard by hand. On Enterprise, Conversation InsightsA Cursor analytics view that passively categorises what agents are doing (new features, bug fixes, refactors) so leaders can see where engineering time goes. Press Enter for the full definition. sorts agent sessions into categories such as new features, bug fixing and refactoring, and it scores how specific each prompt was. Cursor BlameAn augmented git blame that records line-level human and agent co-authorship, so you can trace which code was written by AI versus a person. Press Enter for the full definition., also on Enterprise, marks which lines an agent wrote, so you can check whether agent-written code churns faster than the rest.

Often, yes. On a quick change, plan mode and a saved trail are overhead, so skip them.

The steps above already ask for a plan when a change spans more than one file. Add a second trigger: code you don't already know. Together they draw a sharper line than the usual "anything non-trivial". One security engineer started out skeptical of plan mode and now uses it by default for anything net-new, because in plan mode the agent finds the relevant injection points up front instead of discovering them mid-edit. For quick changes, he still skips it.

On small work, keep one habit: start a fresh agent for each new topic. Continuing yesterday's chat carries all of yesterday's context into today's request, while a fresh agent starts with only the context you give it.