Agentic AI in Operations

I see four key agentic design patterns emerging: reflection (where agents review their own output), tool use (where agents call external APIs), planning (where agents break tasks into subtasks), and multi-agent collaboration. Each of these patterns, applied even to a mediocre base model, can dramatically improve output quality.

The agent paradigm is fundamentally different from the chatbot paradigm. When you give an AI system the ability to use tools, browse the web, write code, and interact with APIs, you are not just building a smarter chatbot -- you are building a digital worker. The companies that understand this distinction earliest will have a massive structural advantage.

Every major tech company is now shipping agent capabilities. Google has agents in Workspace, Microsoft has them in 365 Copilot, Salesforce has Agentforce. The difference between these and last year's chatbots is that they actually do things -- book meetings, file expense reports, update CRM records. The question is how much autonomy you want to give them.

Hot take: the companies failing with AI agents are the ones treating them like software. Agents need operating procedures, escalation paths, and supervision models -- just like human employees. The orgs succeeding are the ones applying operational management thinking, not just engineering thinking.