AI Coding Tools Need Smaller Tasks, Not Less Code Review

AI coding tools can shift engineering work from writing code to verifying it. That is the change described in Trickyworld’s listing for “Coding Agents Need Smaller Tasks and More Code Review”: smaller tasks, focused context and explicit acceptance criteria are the proposed response. My take is that I would adopt that working method before giving an agent a larger share of a production codebase.

I’m working from a category listing, not the full article or a tool’s release notes. It does not establish which agent performs best, how much time teams save, or whether generated changes contain more defects. I would not turn its short description into those claims. What I can do is explain how I would apply its recommendation to work I would actually have to maintain.

Why I would move the boundary from generation to acceptance

I would judge an agent-assisted change by whether I can explain its behavior, not by how convincing its completion message sounds. “Implemented successfully” is not my acceptance criterion. Neither is a large diff that appears to cover every part of the request.

For me, the useful distinction is between producing a candidate implementation and accepting responsibility for it. I can delegate the first while keeping the second. That means I need to understand what changed, which assumptions entered the code, and what evidence supports merging it.

This is where I would apply the source’s advice to newsroom editorial tools. If I asked an agent to adjust a publishing workflow, I would want the review to stay centered on publishing behavior. I would not want to untangle an unrelated component rewrite, a renamed data structure and a new abstraction before I could answer whether the requested change was correct.

My preference is therefore not “use AI for everything small.” It is “make each delegated change small enough that I can reject it confidently.” A task is not well scoped merely because its prompt is short.

Scoping AI coding tools for a reviewable change

I would write the acceptance criteria before the prompt

I would start with the behavior I want, including what must stay unchanged. For a hypothetical story-listing helper, I might require that only explicitly published stories appear, their original order remains intact, and the input array is not modified. Those are example requirements, not details supplied by the source or claims about an existing implementation.

I would also name the non-goals. No changes to the publishing endpoint. No dependency additions. No redesign of the story object. No cleanup of neighboring modules. I want the agent to know where its authority ends, rather than asking it to infer my tolerance for extra work.

My task brief would contain five parts:

1. Goal: the behavior I want to change, described from the caller’s perspective.
2. Allowed scope: the helper and its directly relevant checks.
3. Acceptance criteria: observable outcomes, including boundary cases.
4. Non-goals: changes I explicitly do not want.
5. Stop condition: assumptions or missing information that should trigger a question instead of an implementation.

I would treat a question from the agent as useful output. If the task depends on whether an unpublished story can have a publication timestamp, I would rather resolve that ambiguity than receive code built around an unspoken answer.

I would supply focused context, not an architectural autobiography

I would include the existing helper, its callers where relevant, and the conventions needed to make the change fit. I would leave unrelated product plans and modules out of the initial request. The source recommends focused context; my interpretation is to supply enough information to establish the contract without making every nearby concern part of the assignment.

I would not confuse limited context with permission to guess. If the agent needs another file to understand the contract, I would let it identify that gap. My boundary would be: inspect what is necessary, explain why a wider change is needed, and wait before expanding the implementation.

I would keep the example contract executable

For the hypothetical listing task, I could express a candidate implementation and a few acceptance checks with plain JavaScript. These names and fields are illustrative, not an API from the source. I would adapt them to the actual application contract before using them.

function selectPublishedStories(stories) {
  const selected = [];

  for (const story of stories) {
    if (story.status === 'published') {
      selected[selected.length] = story;
    }
  }

  return selected;
}

function checkPublishedStoryContract() {
  const draft = { id: 'draft', status: 'draft' };
  const first = { id: 'first', status: 'published' };
  const unknown = { id: 'unknown' };
  const second = { id: 'second', status: 'published' };
  const input = [draft, first, unknown, second];
  const output = selectPublishedStories(input);

  return {
    excludesOtherStatuses:
      output.length === 2,
    preservesOrderAndReferences:
      output[0] === first && output[1] === second,
    leavesInputUnchanged:
      input.length === 4 &&
      input[0] === draft &&
      input[1] === first &&
      input[2] === unknown &&
      input[3] === second,
    acceptsEmptyInput:
      selectPublishedStories([]).length === 0
  };
}

I would expect every returned check to be true, but I would not present this as a complete test suite. It makes a deliberately narrow contract visible. It does not decide how malformed inputs should behave, whether additional statuses belong in the result, or whether this filtering belongs at this layer.

Those omissions are part of the exercise. I would resolve them from the application’s requirements, not ask the agent to invent a policy and hide it inside defensive-looking code.

Reviewing AI-generated changes before I merge

I would review the diff against the original brief before reading the agent’s explanation. My first question would be whether the patch stayed inside the agreed boundary. An unexpected file change would need a reason, even if the change looked individually sensible.

Next, I would trace the requested behavior through the implementation. For the example above, I would inspect the exact status comparison, the order of iteration and the handling of the input. I would want to explain the result myself rather than repeat the generated summary.

I would then examine the checks independently. Do they reflect the requirement I supplied, or merely confirm the implementation the agent chose? I would write down a few expected outcomes before accepting generated checks, especially where the task involves permissions or a business-rule exception.

For a newsroom publishing change, I would pay particular attention to what an editor is allowed to do and what remains unavailable. That is where I would spend my own review effort, rather than treating tidy component code as sufficient evidence that the workflow is ready.

I would finish by running the project’s relevant checks and reviewing any remaining uncertainty. If I could not explain a changed branch or assumption, I would ask for a smaller patch or investigate it myself. I would not ask the same agent for another reassuring summary and count that as independent verification.

What smaller AI coding tasks do not solve

I would not treat task size as a substitute for understanding the system. A tiny patch can still encode the wrong business rule. My reason for preferring smaller tasks is that I expect to inspect their assumptions more directly, not that I consider them automatically safe.

I would also avoid turning every meaningful change into isolated fragments. For a change spanning authorization, data handling and the interface, I would first write down the end-to-end contract. Then I would divide the implementation while keeping that contract visible. I would not accept individually plausible pieces without reviewing how they fit together.

The trade-off I expect is more preparation and more explicit handoffs. I would have to write acceptance criteria, answer questions and reject unnecessary changes. If I wanted an agent primarily to remove those responsibilities, this approach would disappoint me. I see it as a way to delegate implementation, not ownership.

I would give this workflow less weight for a disposable experiment where I only need to explore an idea. I would also skip agent delegation when explaining and checking a trivial change would take me more effort than writing it. I would not make tool usage a requirement for its own sake.

My next step would be one bounded task with a written contract and a review I could defend without referring to the agent’s confidence. If I found the verification harder than doing the work myself, I would narrow the task again—or keep that part of the implementation in my own hands.