For complex voice agent systems, a modular approach with smaller, focused sub-agents wins on maintainability and iteration speed, especially when integrating with services like Twilio. Isolated testing of individual components makes debugging specific failures far easier. To make sure your architecture holds up and to catch regressions fast, robust end-to-end conversation testing and production monitoring are essential. We built cekura.ai for exactly that: simulating callers, evaluating conversation quality, and catching regressions for voice AI agents.