AI agents are hitting the same inflection point.
Most people think an agent is the model, the runtime, or the loop currently executing the task. Those things matter. But they aren't the agent.
In this piece I'll argue that an agent is its data: specifically its history of events, which we'll call the log. When the log is constructed correctly, an agent can be resumed from it alone. And that single property unlocks a whole set of capabilities that make even advanced agent use cases easier to reason about and build into everyday applications.
Defining the Agent
So what is the log? Defining the agent means defining the log. At its core, the log is the event history: every user input, model output, tool call, and tool result that accumulates as the agent works — an append-only record of everything that happened.
