History may look back at November 2023 as the true start of the Agentic Era. Before this, we had chatbots—partners in conversation, entities you could talk to but that couldn't do anything beyond generating text. They were advisors, not actors. They could tell you how to accomplish something, but they couldn't accomplish it.
Now, we have agents—partners in action. The boundary between thought and deed has been breached.
The Tool-Using Turn
The critical capability is tool use. The ability for an AI to call external functions, to browse the web, to write and execute code, to access databases, to send messages. This turns the LLM from a philosopher into a worker. It's the difference between a library (where you read about things) and a laboratory (where you do things).
Consider the difference. Before tool use, you might ask "How do I check if this website is working?" The AI would explain about ping commands, HTTP status codes, browser developer tools. You'd have to do the actual checking yourself. After tool use, you can say "Check if this website is working." The AI executes the check and tells you the result. The doing is delegated.
This extends across countless domains. Research that required you to read and synthesize can be delegated. Data analysis that required you to write code can be delegated. Administrative tasks that required you to navigate interfaces can be delegated. The AI isn't just advising; it's doing.
The Conversational Interface to Everything
What emerges is a conversational interface to everything. Every digital capability that can be invoked through an API or a user interface becomes accessible through natural language. "Schedule a meeting with John for next Tuesday" triggers the calendar API. "Find flights to New York under $500" triggers travel search. "Send a summary of this document to the team" triggers email.
The implications are profound. Currently, every digital task requires you to know how to use the relevant software. You need to know Photoshop to edit images, Excel to analyze data, Salesforce to manage customers. Each application is its own skill to learn, its own interface to navigate.
With agentic AI, you describe what you want in plain language. The agent figures out which tools to use, how to use them, what sequence of steps to take. The expertise is in the agent, not in you. The barrier to doing drops to the barrier to describing.
The Current Limitations
We are just at the beginning. These agents are fragile, forgetful, and often incompetent. They lose track of multi-step tasks. They make mistakes that humans wouldn't. They fail in ways that are hard to predict. The trust they require is often greater than the reliability they deliver.
The memory problem is acute. Current agents don't remember context across sessions well. They can't build on previous work. Each interaction starts relatively fresh. This limits their usefulness for complex, ongoing projects.
The reliability problem is fundamental. When an agent takes an action—sends an email, makes a purchase, modifies a file—errors matter. A chatbot that gives wrong advice is annoying. An agent that sends wrong emails is damaging. The stakes of action are higher than the stakes of conversation.
The Ancestors of Tomorrow
But these limited agents are the ancestors of the automated organizations of tomorrow. Every generation improves. Memories get longer. Reliability increases. Capabilities expand. The agents that today can barely complete a three-step task will, in time, handle complex workflows spanning days or weeks.
The seed of the autonomous economy has been planted. We can see the shape of what's coming: AI systems that manage processes, coordinate resources, and execute plans with minimal human intervention. Not chatbots you talk to, but agents you delegate to. Not advisors, but workers.
November 2023 was the birth. The infant is awkward and limited. But it's alive, it's growing, and its future capabilities are hard to fathom. We've crossed from conversation to action, from chat to agency. There's no going back.