From my experience, the tool choice is actually secondary. What really drives success is:
1. How complex the tasks are
2. How clearly and precisely you prompt for each one
3. How sprawling and interdependent your codebase is
4. The underlying model you’re running on (GPT-5, Sonnet-4, etc.)
If those factors aren’t set up well, you’ll hit walls no matter which agent you pick. I’ve seen teams switch tools thinking “this one will finally work” only to hit the exact same issues. Because the bottleneck was their workflow, not the AI’s raw ability.
Once you tune the prompts, scope tasks properly, and feed the model the right context, most modern coding agents perform surprisingly well. That’s why in our platform, some dev teams can ship entire game prototypes or complex features with LLMs, while others struggle to get a passing unit test out of the same tool.
From my experience, the tool choice is actually secondary. What really drives success is: 1. How complex the tasks are 2. How clearly and precisely you prompt for each one 3. How sprawling and interdependent your codebase is 4. The underlying model you’re running on (GPT-5, Sonnet-4, etc.)
If those factors aren’t set up well, you’ll hit walls no matter which agent you pick. I’ve seen teams switch tools thinking “this one will finally work” only to hit the exact same issues. Because the bottleneck was their workflow, not the AI’s raw ability.
Once you tune the prompts, scope tasks properly, and feed the model the right context, most modern coding agents perform surprisingly well. That’s why in our platform, some dev teams can ship entire game prototypes or complex features with LLMs, while others struggle to get a passing unit test out of the same tool.