An agent goal states a desired change in the world plus evidence that would show completion. Vague wishes become engineerable only after success and stopping conditions are observable.

A Goal Is a Testable Destination

An AI agent observes context, chooses an action, uses a model or tool, and updates what happens next. Its engineered identity lies in this loop and its boundaries, not in a chat-shaped interface.

AI Agent as a Goal-Directed Loop

A closed loop measures an outcome, compares it with a target, and changes the next action. Without a trustworthy measure, repeated self-critique may only reinforce the same error.

Closed-Loop Control Uses Measured Feedback

In this situation—helping a neighbor compare three safe transit routes before 5 p.m.—which choice best applies “A Goal Is a Testable Destination”?

Graph search expands possible states through actions until it finds a goal or exhausts a budget. The representation of state and neighbors determines what solutions can be discovered.

Search Explores Possible States

A useful criterion includes outcome quality plus constraints such as safety, cost, latency, and fairness. Optimizing one number without guardrails invites harmful shortcuts.

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