Managing a dozen AI agents daily sounds chaotic, but one developer has a simple rule: classify each task as generative or verification. Generative tasks, like writing code from specs, can use cheaper, faster models. Verification tasks, such as reviewing output for correctness, deserve the slower, more expensive models. This split prevents waste and keeps quality high. The author also shares how this workflow frees up time for writing and other creative work. For teams adopting AI, this is a cost-effective pattern that scales. Instead of treating top-tier models as a luxury, use them where they matter most. The approach is practical, easy to implement, and directly addresses the common pain point of AI tooling costs. It's a mindset shift from 'use the best everywhere' to 'use the right tool for the job.'
A practical framework for managing multiple AI agents by separating generative and verification tasks, optimizing cost and speed.