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Split Large Features Into One-Session Tasks

Coding agents at scaleEN234 reads

01 · Define the feature as a finished user outcome

Start with the result a user should be able to observe, not with a list of technical components. For example, “A team member can export filtered usage records as a CSV file” is a useful feature statement. “Add an export service” is only an implementation idea.

Write down the user, the trigger, the expected result, and the important failure cases. This gives the coding agent a stable target before it begins inspecting the repository. It also prevents the task from expanding into unrelated cleanup.

02 · Record the current system boundaries

Before splitting the work, identify where the existing behavior lives. Note the relevant routes, services, data models, UI components, tests, configuration files, and external integrations. A coding agent can explore these areas, but a short boundary map reduces wasted investigation.

Separate facts from assumptions. For instance, record whether the application already has CSV utilities, how authentication is enforced, and which test command covers the affected module. Unknowns should become explicit investigation tasks rather than hidden risks.

03 · Separate the feature into vertical slices

Prefer slices that travel through the system and produce a demonstrable increment. A strong sequence for the export example might be:

  • › Add the server endpoint that accepts the supported filters.
  • › Generate a correctly formatted export for a small result set.
  • › Connect the interface control to the endpoint.
  • › Handle empty results, invalid filters, and authorization failures.
  • › Add focused automated tests and update the relevant documentation.

Each slice should leave the codebase in a working state. Avoid dividing the work only by technical layer, such as “build the database layer, then build the API, then build the frontend,” unless each layer can be tested and reviewed independently.

04 · Give every task a narrow contract

A one-session task needs a clear contract. State what the agent may change, what behavior must exist afterward, and what it must not change. Include the likely files or modules, but allow the agent to verify those paths against the repository rather than treating them as unquestionable instructions.

Define acceptance criteria that can be checked directly. “The endpoint returns a CSV response with the selected columns, preserves row order, and returns a validation error for an unsupported filter” is stronger than “finish the export API.” Include examples for normal, empty, and failure cases when the behavior is easy to misunderstand.

05 · Control the size of the coding session

Estimate a task by uncertainty, not just by lines of code. A small change touching an unfamiliar authentication path may be larger than a broad change in a well-tested utility. A useful one-session task usually has one main behavior, a limited set of files, and a verification path that can run locally.

Split again when a task contains several independent decisions, requires simultaneous work across unrelated subsystems, or cannot be explained without a long implementation essay. The task description should be short enough that the agent can keep the objective in working memory while it reads and edits code.

06 · Specify verification before implementation

Tell the agent how success will be established. Name the focused test command, lint command, type check, migration check, or manual workflow that applies. If the repository lacks coverage, require a small test at the same time as the behavior change.

Ask for a final report containing changed files, tests run, and known limitations. This makes incomplete work visible and gives the next task a reliable starting point. Do not treat a successful build as proof of correct behavior; verify the user-visible contract as well.

07 · Sequence dependencies and preserve momentum

Order tasks so each one creates a usable foundation for the next. Start with data and contract decisions that would otherwise cause rework, then implement the smallest end-to-end path, and finish with edge cases, observability, and documentation. Keep unrelated refactoring outside the feature unless it is required for correctness.

For sustained agent use, Quanta can keep multiple coding workflows on one balance. Its pay-as-you-go API relay supports Claude Code, Codex, Gemini CLI, and OpenAI SDK clients through https://api.quantarelay.com. Calls are metered on real tokens, with cache writes and cache reads priced separately, which makes usage easier to inspect while iterating through many focused tasks. You can choose models such as Claude Sonnet 5.5 or GPT-6 Astra according to the task, and referrals earn 19% of referred top-ups. Open a Quanta account when you are ready to run this workflow across your team.

08 · Review the task boundary after each session

After the agent finishes, compare the result with the original contract. If the task succeeded, record the decisions that the next task needs. If it exposed a larger design issue, create a new task with that issue explicitly named instead of silently expanding the current one.

The goal is not to make every task tiny. The goal is to make every session bounded, verifiable, and useful on its own. Large features become manageable when each step has a visible outcome, a precise contract, and a clear handoff.