The Review Bottleneck in AI-Assisted Coding

Implementation can run in parallel, but review and coordination still funnel back through me. That is the limit I have not solved in my AI-assisted coding workflow.

In early 2024, I used AI mostly for autocomplete-level work: snippets, error explanations, and small refactors. By 2026, I was using agents for larger tasks. I still write code when needed, but more of my attention now goes to constraints, review, and the edge cases the tools miss.

Reviewing plans before implementation

I started experimenting with Cline for straightforward tasks such as boilerplate, test scaffolding, and repetitive refactors. I avoided giving it larger tasks because it was easy to get code that looked fine but was wrong in non-obvious ways, and anything non-trivial took too much prompt back-and-forth.

In 2025, I switched to Claude Code and stopped asking for immediate implementation. Instead, I asked for a plan first and reviewed it.

Plans were often overbuilt for the actual problem, and the agent would report "done" before handling edge cases. Plan review gave me a place to question the proposed scope before implementation; it did not remove the need to inspect the result.

Making the work reviewable

A lot of bad output came from missing repository context, so I keep architecture preferences, testing requirements, and coding conventions in the repository guidance. That gives the agent a baseline to work from, while the task brief supplies the specific constraints and non-goals.

I often split planning and implementation into separate passes, turning the brief into checkpoints and running build, lint, and tests at each one.

Costs and limits

Frequent tool calls and retries add up, and agents struggle when a system relies on undocumented history.

I can hand off more implementation, but I still have to decide whether the plans fit the problem and whether the resulting changes are ready.