Agent Evolution Series (5): A Global View — Why the Three Paths Ultimately Converge

TL;DR: The three paths ultimately converge because real tasks simultaneously require understanding the current situation, reusing historical experience, and executing real actions. The endgame for agents is not being better at conversation, but becoming a governed goal-completion system: understanding context, consolidating experience, calling tools, executing tasks, and accepting permissions, auditing, rollback, and user control. This is the final article in the Agent Evolution Series. In the previous four articles, we’ve already deconstructed the three paths separately: the execution path, the self-evolution path, and the personal context path, as well as the final form they may form after convergence. The first three articles were more like looking down each path: what it is, why it emerged, what stages it goes through, and where it ultimately leads. The fourth article put the three paths into the same system, discussing how they combine into a governed Agent Runtime. ...

Agent Evolution Series (4): The Final Form — Governed Agent Runtime

TL;DR: The final form of agents is not one path winning over the others, but the convergence of execution, self-evolution, and personal context into a governed Agent Runtime. This system consists of an understanding layer, a learning layer, an action layer, and a governance layer. Key capabilities include context, memory, skills, tool execution, permissions, auditing, and rollback. This is the fourth article in the Agent Evolution Series. The first three articles discussed three paths: ...