A First Mental Model for Agentic AI

An agent interacts with an environment, gathers information, takes actions using tools, and evaluates the results. This loop gives the agent a way to make progress toward a goal, even when the path is not known in advance.

This simple loop is powerful because it turns a static model into a system that can learn from feedback, correct course, and handle real-world complexity.

while not done:
    observation = observe()
    action = act(observation)
    done = evaluate(action)

Each step in the loop can be simple or sophisticated. Observation might read from a user message, an API, or a database. Action might call a tool or produce a response. Evaluation checks whether the goal is met and informs the next step.

What is an AI agent?

An AI agent is a system that perceives its surroundings and chooses actions in pursuit of a goal. The word system matters: an agent is more than a language model. It includes the model, the tools it can use, the context it receives, and the rules that shape what it may do.

An agent’s environment is the part of the world it can observe or affect. For a support assistant, that might be a conversation, a knowledge base, and a ticketing API. For a coding assistant, it might be a repository, a terminal, and a set of instructions.

The agent loop

The loop is a useful mental model because it makes the system’s behavior inspectable. At each turn, ask three questions: what did it observe, what did it do, and how did it decide whether the result was good enough?

Observe

Observation gathers the information needed for the next decision. That can include the user’s latest message, recent tool results, available files, or a summary of earlier steps. Good observation is selective: more context is not automatically better if it obscures the signal.

Act

An action can be a final answer or a call to a tool. A useful design gives each tool a clear purpose and limits its authority to the work it needs to perform. The system should also handle the ordinary cases where a tool is slow, unavailable, or returns an unexpected result.

Evaluate

Evaluation compares what happened with what the system was trying to accomplish. Sometimes the next observation makes this clear. Other times a separate check is needed, such as confirming that a file exists or that a requested change appears in the output.

Tools and memory

Tools let an agent interact with its environment. A search tool can retrieve information; a file tool can read or write; an API tool can request a change in another service. Tool boundaries should make the effect of an action understandable and reviewable.

Memory is information carried from one step to another. Short-term context helps the system stay coherent within a task. Longer-lived memory can preserve useful facts across tasks, but it needs a clear source, a reason to persist, and a way to be corrected.

Planning and control

Planning breaks a goal into smaller decisions. A plan can help with a task that has dependencies, but it is a proposal the agent should revise when new evidence arrives. A plan that cannot respond to feedback is only a list of guesses.

Control comes from the boundaries around the loop: which actions are allowed, which require human approval, how much work can happen before checking back, and what to do when the system is uncertain. These boundaries are part of the design, not a patch added after the agent is built.

System design considerations

This model also helps locate failure. If the agent chose a poor action, inspect the observation it received and the decision rule it used. If a tool changed the wrong thing, inspect the tool contract and its permissions. If the system continued after the goal was met, inspect the evaluation and stopping conditions.

The loop does not guarantee correctness. It gives us a place to ask better questions, add checks, and decide where a person should remain in control.

Putting it together

An agent is easier to reason about when its observation, action, and evaluation are visible. Start with a narrow task, a small set of well-defined tools, and a clear stopping condition. Then measure where the loop succeeds and where it needs more context, stronger checks, or a human decision.

That is a practical first mental model: not a machine that simply “thinks,” but a system that observes a world, changes something in it, and uses the result to choose what happens next.