Why splitting AI agents into thinkers and doers actually works
Developer discovers splitting AI into planning and execution agents beats monolithic design for complex voice tasks like restaurant reservations.
InfoWorld contributor Jenil Shah encountered a wall with voice AI for restaurant reservations. His single agent kept failing because it was trying to analyze customer requests, research restaurants, and handle live phone conversations simultaneously. The solution: split the work between a context agent that takes time to gather complete information and plan strategy, and an execution agent optimized for real-time conversation.
The context agent has detailed conversations with users upfront, researches options through web tools, and creates execution plans before any calls happen. The execution agent focuses solely on the live interaction, armed with all the context it needs. Shah found this separation improved reliability, made debugging easier, and allowed independent scaling of each component.
Product teams building AI systems should pay attention here. This pattern reflects how humans naturally work on complex tasks—we separate planning from execution. When your AI tries to do deep analysis while maintaining conversation speed, it fails at both. The two-agent pattern gives you specialization without the coordination overhead of larger multi-agent systems, and it's easier to audit each component's decision-making for compliance purposes.
Jenil Shah
