Agent design principles

A system earns the name agent when it is goal-directed, reactive, stateful and autonomous. Each property covers one way a plain model call falls short (see LLM versus agentLLM versus agentA model predicts text. An agent wraps that model in a loop with a goal.Opens in a new tab).

The four

• Goal-directed. It works toward an outcome, not a single reply. The goal decides which step comes next and when to stop.
• Reactive. It responds to its environment: a tool result, a failed call, an input it did not expect. It handles the unexpected by changing course instead of following a script. Anthropic puts it as gaining “ground truth” from the environment at each step.
• Stateful. It remembers what it has done in this task and what it knows about the user. That is memoryAgent memoryWhat an agent remembers: the thread it is in, and what it keeps across threads.Opens in a new tab.
• Autonomous. It decides the steps and runs them without a person approving each one. Once the task is clear, it plans and operates independently.

Take one away

Remove a property and the result is something else. With no goal it is a chatbot. Without reaction it is a script. Without state, every turn starts from nothing. Without autonomy it is a fixed workflow.

Autonomy is the one to ration. More autonomy means more that can go wrong without anyone noticing, which is why human in the loopHuman in the loopA pause in the agent's decision, where a person approves before a high-risk action runs.Opens in a new tab and ejection logicEjection logicCustom code that stops the agent, or hands over to a person, when a condition is met.Opens in a new tab exist. The patterns that deliver the other three are tool useTool useFunction calling gives the model hands: it asks, and code does the work.Opens in a new tab, planningPlanningBreak a high-level goal into steps, store the plan, and work through it.Opens in a new tab and ReActReActReason, act, observe, repeat: planning, tool use and a loop in one pattern.Opens in a new tab.