AI Agent Architecture Explained
An AI agent architecture consists of four main parts that work together. The language model acts as the brain that interprets goals and chooses actions. Tools give the agent ways to reach outside information or change data. Memory stores past steps and results so the agent does not start over each turn. A control loop decides when to stop and when to continue.
These parts are not bolted on randomly. The loop reads the current state, sends a prompt to the model that includes memory, lets the model output a tool call or a final answer, and then updates memory with the outcome. This cycle repeats.
First the model states its reasoning about the current state and goal. Then it names a tool and supplies arguments. The system executes the tool and returns the result as an observation. The model reads that observation and begins the next thought.
Tool Use and Memory Design
Tools fall into three common categories. Search tools retrieve external facts. Code execution tools run calculations or transform data. File tools read and write local documents. An agent gains power when it can call any of these without the user writing the request each time.
Designers must also decide what the agent is allowed to forget. Too much memory bloats the prompt. Too little removes the continuity that makes the agent useful in the first place.
Real-World Limits and Human Supervision
Even with good architecture, agents still fail in predictable ways. Tool outputs contain noise. The model sometimes misreads an observation and goes down the wrong branch. Planning drift grows over long tasks and the agent loses track of the original goal.
Supervision does not remove the value of agents. It simply keeps the system inside safe bounds while the underlying loop and memory improve over time.
Q: What stops an agent from running forever?
A: Implementations set a maximum turn count or require an explicit stop condition before the model can begin another cycle.