Task Decomposition

Decomposition sits at the front of an agent's plan-and-act loop. Before any code is written, the agent has to decide what "done" is made of: which subtasks the goal contains, what each one needs, and which depend on others. Without that step, an agent either tries to do everything in one pass and gets it half right, or wanders through actions with no sense of the whole. It is the same divide and conquer idea that has run through AI planning for decades, now applied to large language model agents working on real software.

The reason it matters more in the agentic SDLC is scale. A single model call can do a small, well-defined step well. It cannot reliably carry a migration across fifty services or a feature from ticket to merged PR. Decomposition is what bridges that gap: it breaks the large request into steps small enough to trust, then lets autonomous agents run them. In Overcut, that breakdown does not have to be improvised on every run. It can be encoded as an agentic workflow or a playbook, so the decomposition is standard, inspectable, and gated rather than reinvented each time.

A continuous integration build starts failing on the main branch. The goal, "fix CI," is not one action, so the agent breaks it down. First it reads the job logs to see which step failed. Then it reproduces the failure in an isolated environment, narrows it to a unit test that broke after an API response changed shape, and decides on a fix. It updates the test fixture, reruns the suite to confirm the build is green, and opens a pull request with the change. Each of those is a subtask small enough for an agent to do and check, and they run in order because each one depends on the result of the last. A human approves the PR before it merges.