Breaking Large Features into Slices AI Coding Agents Can Finish

Give an AI coding agent a large feature in a single request and the result is predictable. The session grows longer, the context fills with sediment, and the decisions get worse the further it goes. The fix is rarely a cleverer prompt. It is the old engineering discipline of cutting work into the right pieces, then deciding which pieces need a person and which do not.

Why ordered phase plans fall short

A common habit is to ask the agent for a multi-phase plan: database first, API second, interface last. It looks tidy, but it has two weaknesses.

First, agents tend to work horizontally, finishing one whole layer before touching the next. Nothing integrated exists until the final phase, so the first real feedback on whether the pieces fit together arrives late, when mistakes are most expensive to unwind.

Second, numbered phases are strictly sequential. Only one agent can pick them up, so there is no room to run work in parallel.

Vertical slices give agents early feedback

The alternative is the tracer bullet idea from classic software practice: build a thin slice of functionality that crosses every layer it needs. A good first slice includes a schema change, a small piece of new service logic and a minimal visible result in the interface. It does not need to be complete. It needs to run end to end.

With a slice like that, the agent can test the entire flow almost immediately, and every later slice adds to something that already works. Agents will still drift towards layer by layer thinking, so a human should read the proposed slices and push back when the first one is just a service with nothing visible on top.

Model the plan as tickets with blockers

Instead of a numbered list, treat the plan as a set of tickets with blocking relationships. Some tickets wait on others, and several can start at the same time. That structure is a dependency graph, and it lets multiple agents work in parallel once the first slice lands.

Each ticket should be small enough to fit comfortably in one fresh session. Starting clean from a ticket, the recent commit history and the codebase is more predictable than compacting a long conversation into a summary and carrying on. It also helps to label every ticket by whether it needs a person in the loop or can run unattended.

Keep people in alignment and agents in execution

Misalignment is the main failure mode, so the human effort belongs up front. A useful pattern is to have the agent interview the team one question at a time, offering a recommended answer to each, until everyone shares the same picture of the feature. Questions such as whether existing records should be backfilled surface decisions nobody had considered.

That conversation is then condensed into two documents:

• A destination document covering the problem, user stories, implementation decisions and testing decisions.
• A journey, meaning the ticket graph described above.

Once both are reviewed, implementation can run unattended, ideally inside an isolated sandbox with limited permissions. Think of it as a day shift of planning followed by a night shift of execution.

One caution: this is not a process where specs are edited and code is ignored. The codebase stays in view throughout, because a messy codebase produces messy agent output.

What this means for web projects

For web teams, the takeaway is practical. Slice features so each ticket touches the database, server logic and interface together. Express dependencies explicitly. Keep sessions short, and spend senior attention on alignment and slice review rather than on watching an agent type. That is where human judgement still pays for itself.