How to make your coding agent faster

Your coding agent is slow because you let it be slow. It reads too much, searches too much, runs too long on the wrong path, or finishes only to discover it misunderstood the task. The result is a bill that grows with every token and minutes that slip by while you wait.

Start with a narrower reading list

Give the agent a focused set of files instead of the whole repository. If it has to scan hundreds of modules to find the two that matter, each extra file adds tokens and time without helping. A targeted list also reduces the chance it will latch onto the wrong part of the codebase.

For a feature change, include only the files you expect to touch plus the tests that cover them. For a bug fix, include the buggy function, its callers, and the test that reproduces it. Everything else is noise.

You already know which files change most often. Pin those in the prompt or in a small manifest the agent reads first. Over time you will learn which docs sections the agent hits repeatedly; move those into a local summary so it doesn’t have to search the repo each run.

Avoid letting it search

When the agent has to look up symbols or usages across the codebase, it performs a search that costs tokens and latency. If the information is already available in a focused summary you provide, it skips the search and stays on task. The fewer times it has to switch from reading to searching, the faster it finishes.

Split the task so each step has a short context

A single run with a hundred files of context is slow by design. Instead, break the work into smaller, independent steps. Each step’s input is small, the agent finishes quickly, and you can rerun or adjust one piece without restarting the whole flow.

Define clear handoffs between steps

For a feature, start with a design step that outputs a short spec and a list of files to change. The next step takes that spec and the targeted files, implements the change, and produces a diff. A third step runs tests on the diff and records the results. Each step’s context is under ten files, so the agent stays fast.

If a step fails or changes direction, you rerun only that step and the ones that depend on it. The rest of the work stays untouched, which beats waiting for a long initial run to finish before you notice the mistake.

Keep each step’s output small

The agent should emit only what the next step needs: a concise summary, a diff, or a test report. Large logs or intermediate artifacts bloat the context for the next run and slow it down. Treat each step’s output as a contract you can hand off cleanly.

Stop the run as soon as it misunderstands

Agents rarely self-correct once they go off track. If the first few messages show the agent editing the wrong files, targeting the wrong issue, or proposing an approach that won’t work, stop it immediately. Letting it continue wastes time and tokens, and the output often becomes noise you still have to clean up.

Watch for these signals

• The agent claims to have found the issue but lists files unrelated to the bug.
• It proposes a change that contradicts the existing architecture or tests.
• It spends multiple turns searching for symbols that should be obvious from the prompt.
• The diff it produces is empty or touches the wrong parts of the codebase.

When you see these signs, pause the run and ask for clarification or a corrected plan. If the misunderstanding is minor, you can nudge it back on course. If it’s fundamental, you save more by stopping early than by waiting for a partial result.

Avoid the sunk-cost trap

Agents do not magically recover from early mistakes. Every extra minute you let it run compounds the cost. Treat the run as a hypothesis to test: if the first output is wrong, the hypothesis is invalid and you should pivot or stop instead of doubling down.

Put it together: a faster workflow

Start by listing only the files that matter for the task. Provide a short summary of the relevant design and APIs so the agent doesn’t have to search. Then break the work into small, independent steps with clear inputs and outputs. Run the first step and review its output immediately. If it’s correct, move to the next step. If it’s wrong, stop, clarify, and rerun only what’s necessary.

This approach cuts token usage by focusing the agent’s context, avoids expensive searches, and prevents long runs on wrong paths. The result is faster cycles, lower bills, and fewer surprises when the agent finally finishes.

The fewer files an agent reads, the faster it finishes, and the cheaper the run.

When coordination slows you down

If you run multiple agents on the same task, their interactions can add latency. Each agent waits for the other to finish before it starts, or they step on each other’s changes and create merge conflicts. That coordination overhead can dwarf the savings from parallelism.

One agent can do real work while the others watch and learn, but only if you give it a focused task and a short feedback loop. Otherwise you end up with a slower, more expensive process than a single agent handling the same work alone.

If you need handoffs between agents, keep each handoff a small, well-defined step. Provide the state of the work so the next agent doesn’t have to reconstruct it, and set a clear success criterion so you can stop early if it goes wrong.

For a deeper look at how agents interact without merge hell, see Running multiple coding agents at once without merge hell.

Measure what actually matters

Speed is not just seconds on the clock. Track token usage, total run time, and the number of reruns or fixes you have to make. If your agent spends more time searching than implementing, you have not made it faster; you have only hidden the cost.

Set a simple rule: if a run takes more than five minutes or produces output that still needs heavy editing, reconsider the prompt, the files, or the task split. The goal is to finish the work with minimal back-and-forth, not to squeeze every last second out of the agent.

For a checklist of practices that keep agents aligned and review-ready, see How to review AI generated code.

The tools you already have

You do not need a new agent to go faster. The slowness comes from how you set up each run. Narrow the context, give the agent the answer up front, split the work into small steps, and stop early when it misunderstands. Those four changes cut run time and cost without changing tools.

If your agents run in a shared workspace where their context and decisions are visible as they work, you can spot misunderstandings faster and rerun only the affected parts. The five tools each contribute to that visibility in a different way.

For a deeper look at how those tools sequence around a task, see How the five tools work together.

Supported agents include Claude Code, Cursor, Codex, and any other MCP-capable agent. You call them; the workspace coordinates their work. See Supported agents for details.