AI can do the grunt work here, but the architectural calls need a human engineer.

Each of the 6 hierarchies likely has a different shape, depth, and state topology, so the agent can't apply a single mechanical template. There is structural similarity in the refactor pattern, but each instance requires reading the specific component tree and making local decisions.

Success criteria are underspecified: 'cleaner state management' and 'performance issues' are not measurable targets. The agent cannot know whether it has solved the performance problem, chosen the right pattern (Context+useReducer vs Zustand), or drawn state boundaries correctly without explicit benchmarks or acceptance tests.

The agent needs full read/write access to the codebase, the ability to run tests, and ideally a way to measure render performance before and after. If given repo access and a test suite, the mechanical execution is feasible; without tests or performance baselines, the agent cannot verify correctness.

These are described as 'critical' hierarchies, meaning a subtle bug — a stale closure, a missing dependency, a broken context boundary — could cause silent regressions in production. Refactors of this scope are reversible via version control, but detecting the error before it ships requires careful human review.

Choosing between Context+useReducer and Zustand, deciding where to draw state ownership boundaries, and ensuring the documented pattern is actually teachable to the team all require architectural taste and knowledge of the team's conventions that an agent cannot infer from code alone.