In a recent LinkedIn post, Betsy Tong draws a compelling analogy between the intricate operations of a busy restaurant kitchen and the functionality of modern AI agent systems to explain how scalable intelligence is achieved. Tong uses the dinner rush at a restaurant as a relatable example to illustrate how multiple specialized agents, working in coordination, can achieve a complex output more effectively than a single, all-encompassing entity.
“The grill station doesn’t talk to the sauté station. They each get a ticket. They each run their job. Nobody coordinates. Nobody waits.”
This vivid description, according to Tong, mirrors the challenges and solutions found in AI agent systems. She posits that the perceived seamlessness of restaurant staff is not due to individual brilliance but rather a well-designed system of specialized roles, much like an expeditor coordinating the final plating and delivery of dishes.
The Multi-Agent System Explained
Betsy Tong highlights that just as a restaurant kitchen has distinct stations for different tasks, AI systems are increasingly employing a multi-agent approach. She breaks down how this applies to practical AI use cases.
Legal Contract Review
In the context of legal work, Tong explains that an AI system doesn’t rely on one agent to do everything. Instead, she outlines a process where different agents handle specific parts of the task:
“A legal AI reviews a contract: ↳ One agent reads it. ↳ One flags the risk clauses. ↳ One compares it to your templates. ↳ One surfaces what your lawyer actually needs to read.”
This division of labor, Tong argues, ensures that each step is handled by an agent optimized for that particular function, leading to a more efficient and accurate outcome. The final output is a synthesized result from these specialized stations, much like a completed plate of food emerging from the kitchen.
Customer Service and Payment Processing
Tong extends this analogy to customer service and payment processing AI. For customer service, she notes:
“A customer service AI handles a complaint: ↳ One agent reads the ticket. ↳ One pulls the order history. ↳ One drafts the reply. ↳ One decides whether to send it or escalate.”
Similarly, for payment AI, Tong describes a workflow where agents check contract terms, identify contradictions, and schedule payments. She emphasizes that in these scenarios, the effectiveness comes from the system design rather than a single super-intelligent agent.
Intelligence at Scale: A Kitchen Analogy
The core message from Betsy Tong is that true intelligence at scale does not resemble a single, brilliant mind. Instead, it mirrors the dynamic and coordinated environment of a busy restaurant kitchen during peak hours.
“Intelligence, at scale, looks less like one brilliant brain. It looks more like a kitchen at 7:35pm on a Saturday.”
As Tong points out, the success of such a system lies in each component (or agent) performing its specific role effectively, with a central coordinator (like the expeditor or a system orchestrator) ensuring the final output meets the required standard and is delivered in a timely manner. This approach, she suggests, is key to designing AI systems that can handle complex tasks efficiently and reliably.
📝 About This Content
This article is based on insights shared by Betsy Tong on LinkedIn.
📅 Originally posted on October 3, 2026 | View original post on LinkedIn →