The AI Agent Coding Workflow I Use Now

After a few months of experimenting, here’s the AI-agent coding workflow that’s worked best for me so far — especially as a solo developer who needs to be efficient with limited time.

1. Start with context, not a direct code request. Before asking the agent to build something, I give context: what already exists, why this change is needed, what constraints have to be respected. The clearer the context, the less often I need to correct the result.

2. Small tasks, quick review. Instead of asking for “build me the whole feature X,” I break it into small tasks I can review one by one. That keeps me aware of what’s changing in the codebase, rather than blindly trusting the output.

3. Let the agent explore the codebase itself. Modern agents can read files, find existing patterns, and follow the conventions already in use. I don’t need to re-explain the project structure every time — just make sure it reads first before writing.

4. Verify, don’t just trust. Every meaningful change I build and test manually before committing. AI can “hallucinate” — sound confident while being wrong. Testing stays my responsibility.

5. Keep architectural decisions in my own head. Strategic things — stack choices, data structures, design trade-offs — I still decide myself. The agent executes, I direct.

This workflow isn’t about “AI does everything” — it’s about how I can focus on the decisions that matter, while AI handles the more mechanical execution.