Development-grade planning: PR-sized tasks your AI agents can build
For software goals, TaskNeuron plans at engineering depth — vertical-slice tasks the size of a pull request, each with an approach, acceptance criteria, and a definition of done.
By TaskNeuron Team
Most AI project plans are fine for a human who already knows the codebase and useless for anyone — or anything — that doesn't. "Build the auth system" is a wish, not a task: it doesn't say what to build first, what done looks like, or which files it touches. A person with context can fill those gaps from memory. An AI agent can't, a new teammate can't, and even you can't six weeks later. For software projects, TaskNeuron's planner works at a different resolution: it produces development-grade plans made of tasks an engineer, or an AI agent, can actually pick up and finish.
What development-grade means
When you describe a software goal, the planner breaks it into PR-sized, vertical-slice tasks — each one a coherent change that spans the layers it needs rather than a vague area of work. It's the difference between a to-do list and a spec you can execute against.
The anatomy of a task
Concretely, each generated task carries engineering guidance, not just a title. There's the approach — how to tackle the change. There are the files and areas it's expected to touch, so you know its blast radius before you start. There are acceptance criteria that define what the change must actually do. There are the tests that should exist to prove it. And there's a clear definition of done, so "finished" is a fact you can check rather than a feeling. Read one of these tasks and you know exactly what you're signing up for.
Why vertical slices beat layers
A lot of planning tools slice work horizontally — "do all the database, then all the API, then all the UI." That produces long stretches where nothing actually works end to end. A vertical slice instead cuts through every layer a single capability needs: a bit of schema, the API to serve it, the interface to use it, and the tests around it. Each slice leaves the product working and a little more capable than before, which is exactly what makes it safe to review and ship on its own.
Why the task size matters
PR-sized is the whole point. A task scoped to a single pull request is reviewable in one sitting, testable in isolation, and safe to ship without dragging half-finished work along with it — and it's exactly the unit an AI coding agent can hold in its context and complete in one pass. Plans made of enormous, fuzzy tasks can't be delegated to anyone, human or machine; plans made of tight vertical slices can be handed off, parallelized, and executed. Task size isn't cosmetic — it decides whether a plan is executable at all.
This is what makes the loop possible
Development-grade planning is the foundation the rest of the platform stands on. Connect an AI assistant over MCP and it can request the next actionable task and know precisely what "done" means, because the acceptance criteria are right there in the task. Wire up GitHub and each task tracks the pull request that fulfills it. The plan becomes an execution queue: agents draw a task, implement it against its brief, and open a reviewed PR — with acceptance criteria to check the work against. None of that works if the tasks are vague. It all works when they're precise.
You still hold the pen
Precise plans aren't rigid ones. Every task is a draft you can edit — retitle it, tighten the acceptance criteria, drop a slice that turned out unnecessary, or expand one into subtasks when it's bigger than it looked. The planner gives you a strong first draft at engineering depth; you shape it with the judgment only you have about your product and your constraints.
And nothing an agent produces ships without your review. The loop is built so the machine carries the labor and the human holds the decisive gate: you approve the merge. Development-grade planning gives the machine enough structure to build; it doesn't take the judgment away from you.
The quality of an execution plan is capped by how buildable its tasks are. Plan at engineering depth, and the same plan that guides your team can also feed the agents that help build it — the plan and the build become two views of the same work.