Agent workflow comparison. Spec first vs prompt first across GPT 5.3 Claude Opus 4.6 Gemini 2.5 and DeepSeek

I have been testing agent workflows for a few weeks and I think the real difference is not the model but the process

I tried the usual prompt first vibe approach. One long prompt. Tons of context. Ask the agent to ship a feature end to end. Day one feels insane. Day five the project starts drifting. UI changes for no reason. API shapes mutate. Random refactors appear. I spent more time policing than building

So I ran a small comparison using the same mini SaaS style project and the same rules. Models I touched were GPT 5.3 and GPT 5.2. Claude Opus 4.6. Gemini 2.5. and DeepSeek for some smaller tasks. Then I compared two approaches

Approach one. Prompt first. The agent decides the structure as it goes. Fast start. Higher drift. More cleanup

Approach two. Spec first. One page spec with goal. non goals. files allowed. API contract. acceptance checks. rollback rule. I used Traycer AI to turn my messy brain dump into a clean checklist spec. Then I used Cursor and Copilot for day to day edits and Claude Code for bigger refactors. For multi step loops I tried LangGraph style flows where each step had a stop condition like tests passing or a UI snapshot match

The result was honestly boring in a good way. Spec first made every model look smarter. Even weaker models stopped hallucinating because the scope was boxed in. And the stronger ones stopped freelancing because the acceptance checks made drift obvious

My current take. If you are building anything beyond a toy. treat the agent like a junior engineer on a strict ticket. Small scope. diff only. tests. ship in increments. The model is the engine but the spec is the steering wheel

Curious how others here do it. Do you keep one master spec or slice specs per feature. And how do you handle session memory so the agent does not forget decisions across weeks

LMK guys ...