Stepwise Task Execution
Summary
A technique for having an AI agent execute a task list one item at a time, with an explicit pause-and-approve checkpoint after each step. The AI completes a sub-task, marks it done (checking off the Markdown checkbox), then stops and waits for the human to type “yes” before proceeding to the next item. This one-at-a-time cadence catches errors, linter issues, or wrong approaches before they compound across subsequent tasks.
The key insight: in AI-assisted development, errors are cheap to fix at step N but expensive to fix at step N+10 (because later steps build on earlier ones). Stepwise execution with checkpoints is an error-containment strategy — it trades speed for correctness at exactly the points where the tradeoff is worth it.
How to Apply
When to use: Multi-step AI execution where later steps depend on earlier ones. Especially important for code generation, database migrations, or any sequence where a mistake in step 3 silently corrupts steps 4-10.
When not to use: Independent, parallelizable tasks where each item can be evaluated in isolation. In those cases, parallel execution (e.g., running multiple agents simultaneously) is better.
Steps:
- Create a
task_list_management.mdrule file that instructs the AI to: read the task list, start with the first unchecked sub-task, complete it, mark[x]in the file, then stop and wait - @-include the rule file and the TASKS.md file
- The AI starts with the first sub-task, makes the required changes
- After completion, AI edits TASKS.md to check off the box and pauses
- You review the output (check the code, run tests, inspect the change), then type “yes” or “y” to continue
- If something is wrong, you provide corrections before the AI proceeds
The compounding error problem: When AI agents execute multi-step plans without checkpoints, a small mistake in an early step (wrong schema, bad assumption, misunderstood requirement) cascades through everything built on top of it. By step 10, you’re debugging a tangled mess rather than fixing one wrong assumption. Stepwise execution prevents this by catching issues at the source.
For AI PMs: This is a concrete implementation of the “human-in-the-loop” pattern that AI products often need. The lesson: the checkpoint doesn’t need to be sophisticated — a simple “yes to continue” gate is enough. The UX question for AI products is: where do you place the checkpoints? Too many and the user is babysitting; too few and errors compound. Carson’s approach (one per sub-task, not per sub-sub-task) is a practical middle ground.
Sources
From: 2026-02-07 Ryan Carson Structured AI Development
Key quote: “This back-and-forth, one-step-at-a-time process is what makes the whole system so reliable. It lets Ryan catch small errors, linter issues, or other mistakes before they become bigger problems.”
Attribution: Ryan Carson
What this source adds: Carson’s task_list_management.md rule file implements this as a concrete Cursor workflow. The mechanism is simple: the AI edits the TASKS.md file to check off boxes, creating a visible audit trail. The “yes to continue” UX is minimal but effective — it keeps the human in control without requiring deep engagement at every step.
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