How to Keep AI Coding Agents Focused

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.

A model has only so much attention to spread across a large context, much like a person. The more you put in, the thinner that attention spreads, and quality drops. Cursor's field engineers describe output quality as two levers, the model you pick and the context you supply, and weight them roughly 60/40 to 70/30 in favor of model choice. Context is the smaller share, but it is the lever you control.

When the window gets close to full, Cursor compresses older parts of the conversation into a summary. The same field engineers warn that compaction near the limit behaves like missing context: the agent works as if it never had the detail, while you still pay for all the tokens. You get the cost of a full window without its benefit, so don't run a chat to its limit on purpose.

Summarize and hand off

Before you hit the limit, ask the agent for a short summary of what it has done, then paste that summary into a fresh agent and continue. Or point the new agent at the old chat with @Chats, so it reads only the parts it needs. The next agent doesn't need the chat back-and-forth. It needs the codebase, the commits and the new code the previous agent left, so treat the codebase as the source of truth instead of the chat.

Start a new agent whenever the task changes. Continuing the same task in one session is fine, but a different feature, file area or question deserves a fresh agent. Field engineers repeat a simple rule: be specific, be precise and stay isolated. A new agent discards prior history, which helps you, because stale context can throw the model off its tracks.

Treat each chat as a task-by-task agent. You don't run a whole project end to end in one window. You break the work into logical steps: build the foundational architecture in one agent, then open a new chat for the landing page, and another for a single tab or feature. Each context window then holds about one topic, and switching tasks is also the natural moment to switch models. Stay modular, and keep each chat light.

A near-empty window also protects your rules. A presenter in Cursor's QA workshop found that after a very long conversation, rule context can be silently overwritten as the window fills, and the agent stops following a rule it was given. If a scoped task, such as running tests, keeps going off-pattern, open a fresh agent that does only that one thing, with a lean, specific rule and nothing else competing for attention. For verification, add a validator: a skill or sub-agentA child agent a main agent spawns to work in parallel with its own context window, handing results back so the parent's context stays clean. Press Enter for the full definition. that runs the task, reads the output and reruns it to confirm the real result shows up.