The short version
An AI agent is a software system that repeatedly uses a model to choose its next step. A useful way to picture one is as a digital worker: the harness supplies tools, rules, and access to persistent information, while the model predicts what action should happen next from the context it can currently see.
The model may live in the cloud or run locally. What makes the system useful is not the metaphorical brain by itself, but the way the surrounding software assembles context, limits actions, executes tools, and checks results.
System map
The agent execution loop
What is actually happening?
At the start of a task, the harness collects the current goal, instructions, conversation history, retrieved information, and available tool definitions. That bundle becomes the model’s active context. The model then produces the next response or tool call based on the information and constraints in that context.
If a tool is called, the harness executes it and adds the result to the next context. The loop can continue until the task is complete, a policy blocks an action, or a human takes over.
Three things to keep separate
Context is not memory
The context window is the capacity of the active working surface. Its size is limited by the selected model, and its contents change from step to step. A database, file, or vector store can hold persistent information, but the harness must deliberately retrieve useful pieces and put them into the active context.
Prediction is not a guarantee
The model is very good at continuing patterns in language and tool-use examples, but a confident response is not proof that an action is correct. Production systems need validation, permissions, evaluation, and observability around the model.
Tools define the action space
An agent with no tools can only produce information. An agent with unrestricted tools can create unacceptable risk. The useful middle is a small, explicit capability surface with clear input validation, authorization, and failure handling.
Use this model when designing a system
When an agent behaves unexpectedly, inspect the system in this order:
- What goal and rules were placed in the active context?
- What information was retrieved, and what important information was missing?
- What tools and permissions were available at that step?
- What result came back from the tool?
- What evaluation or human review would have caught the failure?
That sequence turns a vague question about whether a model is “smart enough” into a set of engineering questions that can be tested.