How did large language models lead to the development of agentic AI?

How did large language models lead to the development of agentic AI?

Large language models helped make agentic applications more flexible by providing a general interface for interpreting instructions and producing structured actions. A model can translate a user's request into a possible next step, then use the result of that step as context.

Agents existed before LLMs. The newer development is using broadly trained language models within the control loop.

From generating answers to selecting actions

A basic chatbot may answer a question using the context it already has. An agent can decide that it needs more information and invoke an available tool.

For example, an assistant investigating a failing software test could inspect the failure message, open relevant files, propose a change, run the test, and inspect the new result. The surrounding application performs the actual file and test operations.

Research such as ReAct explored combining language-model reasoning with actions and observations in interactive tasks.

Instructions and observations form a loop

A typical agent receives a goal, chooses an action, observes the result, and decides whether to continue. Application code manages state, tool execution, and stopping conditions.

This makes agents different from merely asking a model for a numbered plan. A written plan describes actions; an operating agent carries out permitted steps and responds to what happens.

Reliability remains a system problem

A model may choose an irrelevant tool, misunderstand its output, or continue after it should stop. Clear tool definitions, bounded permissions, evaluation, and error handling help manage those failures.

LLMs made more workflows expressible through natural language, but a capable agent requires more than fluent text. Its actions must be appropriate, its observations trustworthy, and its final claims supported by the work it actually performed.