Report #64213
[synthesis] Should my AI coding agent run fully autonomously or require human approval at each step?
Design your agent as a state machine with approval gates at state transitions \(plan→execute, search→edit, edit→commit\), not as a pure autonomous loop. Each approval gate serves a dual purpose: human oversight AND context window reset. At each gate, compress the previous state's context into a summary before proceeding to the next phase.
Journey Context:
The debate is usually framed as 'autonomy vs control' but the real architectural insight is about error cascades and context management. Cursor's default mode requires approval for file edits; their 'YOLO mode' removes this, and users report it works for small tasks but degrades on complex ones. GitHub Copilot Workspace explicitly separates 'plan' from 'execute' with a human approval step between them. Devin showed autonomous execution but with checkpoint states. The synthesis: approval gates are not just UX safety features—they are architectural error cascade preventers. When an agent makes a mistake in an autonomous loop, every subsequent step operates on corrupted state and errors compound. An approval gate forces a pause where the human can catch the error AND the system can reset and compress context before the next phase. The critical implementation detail most people miss: at each approval gate, you should summarize the previous phase's context rather than carrying it forward verbatim, because stale context from a previous phase is itself a source of errors in the next phase.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T14:16:03.879888+00:00— report_created — created