How to Keep AI Coding Agents Focused

The working pattern for agent focus control is small enough to review and specific enough to repeat. Give the agent a named task and only the context it needs, then tie the result to a check you can rerun. The table below breaks that into moves.

Each of those moves fails in its own particular way, and the order they come in is doing more work than it looks like it is. Skip the boundary and you get a diff nobody wants to read, because the agent has quietly rewritten files you never meant to open. Thin context fails more quietly: the code compiles, the tests pass, and it ignores every pattern the rest of the repo follows. Then there's the check, which to me is the real one, because it's the whole difference between a result you verified and a result you're taking someone's word for.

The step people skip is the plan. I think it's because it feels like overhead, thirty seconds of nothing visibly happening while you're trying to get work done. Actually, that's not quite the reason. It's that the cost of skipping it lands much later, so it never registers as the mistake it was. If the plan names files you didn't expect, you've learned something for free. If it names the right ones, you've got a reference to check the diff against when it arrives. And once there are four hundred lines on the screen, changing the approach means throwing that work away, which nobody is good at.

Running agent focus control as a team comes down to one habit: leave a trail the next reviewer can follow. The steps below keep the prompt and its proof attached to the change, so nobody has to reverse-engineer what the agent did.

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.

Keep whatever lets you rerun the work or hand it to someone else. A finished task is the merged code plus the short trail that explains 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.

A model has only so much attention to spread across a large context - much like a person. The more you stuff in, the thinner that attention spreads, so quality degrades. Output quality comes down to two levers: the model you pick and the context you supply, weighted roughly 60/40 to 70/30 in favor of model choice. Context is the strong minority - and it is the lever you control.

When you exceed the limit, the agent compacts prior context. Compaction near the limit behaves like missing context - the agent acts as if it never had the detail - while you still pay for all the tokens. That is the worst of both worlds, so don't run a chat to its limit on purpose.

Summarise and hand off

Before you hit the limit, ask the agent to summarise what it has done, then paste that small summary into a fresh agent and continue. The real context the next agent needs is not the chat back-and-forth - it is the codebase, the commits and the new code the previous agent left. Treat the codebase, not the chat, as the source of truth.

Whenever the task genuinely changes. Continuing the same task in one session is fine; switching to a different feature, a different file area, or a different question deserves a fresh agent. The rule field engineers repeat is simple: be specific, be precise, and stay isolated. A new agent discards prior history, which is to your benefit - stale context can throw the model off its tracks.

Think of each chat as a task-by-task agent. You don't run a whole project end to end in one window - you break work into logical beats. Build the foundational architecture in one agent, open a new chat for the landing page, another for a single tab or feature. Each context window then holds essentially one topic. Switching tasks is also the natural moment to switch models. The rule of thumb: stay modular and stay lightweight.

Keeping the window near-empty also protects your rules. After a very long conversation, rule context can get silently overwritten as the window fills - the agent stops following a rule it was given. If a scoped task (running tests, say) 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 you can add a validator process - 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 to confirm the real result surfaces.