agentic coding workflow reframe
iOS veteran finds 15 years of coding experience transfers to AI agents only through planning and review discipline XRealityZoneTL;DW
- Agent-driven development requires embracing Git commits as checkpoints, not just quality gates—revert and retry becomes powerful when agents can learn from commits.
- Running multiple agents on one project beats context-switching between projects; focused agent teams produce more tangible progress than scattered parallel work.
- Plan mode before execution prevents costly 20-minute agent detours; upfront clarity via detailed plans saves far more time than post-hoc fixes.
- Use a three-layer quality gate: sub-agents do work, external model reviews PRs (bias-free), automated tests verify functionality—each model has blind spots.
- Give AI visibility via Model Context Protocol (MCP) tools and detailed JSON annotations; Xcode Instruments data, analytics APIs, and image metadata amplify agent effectiveness.
- Commit frequently and keep PRs small after agent tasks; massive PRs become unmergeable—break them into reviewable chunks even after completion.
- Prevention beats correction; precise upfront prompts prevent agents from hacking tests or optimizing the wrong thing; invest prompt quality early.
- Experienced developers remain essential gatekeepers; 15 years of iOS knowledge catches what AI misses, especially in architecture, security, and codebase context.
- Automation multiplies over time—every "don't forget" task is a candidate for CI automation; tools like Cursor support weekly autonomous improvements via agents.
- Use Agent Skills (specialized context prompts) for your weak areas, not coding; marketing psychology, ASO, and performance analysis yield more ROI than code skills.
TL;DW
- Agent-driven development requires embracing Git commits as checkpoints, not just quality gates—revert and retry becomes powerful when agents can learn from commits.
- Running multiple agents on one project beats context-switching between projects; focused agent teams produce more tangible progress than scattered parallel work.
- Plan mode before execution prevents costly 20-minute agent detours; upfront clarity via detailed plans saves far more time than post-hoc fixes.
- Use a three-layer quality gate: sub-agents do work, external model reviews PRs (bias-free), automated tests verify functionality—each model has blind spots.
- Give AI visibility via Model Context Protocol (MCP) tools and detailed JSON annotations; Xcode Instruments data, analytics APIs, and image metadata amplify agent effectiveness.
- Commit frequently and keep PRs small after agent tasks; massive PRs become unmergeable—break them into reviewable chunks even after completion.
- Prevention beats correction; precise upfront prompts prevent agents from hacking tests or optimizing the wrong thing; invest prompt quality early.
- Experienced developers remain essential gatekeepers; 15 years of iOS knowledge catches what AI misses, especially in architecture, security, and codebase context.
- Automation multiplies over time—every "don't forget" task is a candidate for CI automation; tools like Cursor support weekly autonomous improvements via agents.
- Use Agent Skills (specialized context prompts) for your weak areas, not coding; marketing psychology, ASO, and performance analysis yield more ROI than code skills.
Antoine van der Lee details the workflow shift: detailed upfront specs, Git checkpointing between agent runs, MCP tooling for analytics visibility, and strict PR review gates. Engineering judgment on security and architecture remains essential—just applied earlier in the planning phase rather than at the keyboard.