Agentic AI has become one of the most discussed topics in the field — but there is significant confusion about what agents actually are, how they work architecturally, and what they can and cannot reliably do.
This post provides a technical, grounded explanation of agentic AI systems.
What Is an Agent?
An AI agent is a system that perceives its environment, maintains a representation of state, takes actions to achieve goals, and updates its strategy based on feedback. This is distinct from a language model, which maps input tokens to output tokens without persistent state or goal-directed behavior.
The ReAct Architecture
The dominant paradigm for LLM-based agents is ReAct (Reasoning + Acting), introduced by Yao et al. in 2022. In ReAct, the model alternates between reasoning steps (Thought) and action steps (Action/Observation) in an interleaved format.
Each reasoning step allows the model to think through what it knows, what it needs to find out, and what action to take next. Each action step involves calling a tool — web search, code execution, API call, or file system operation — and receiving an observation.
Memory in Agentic Systems
Long-horizon task execution requires memory beyond a single context window. Modern agent architectures implement multiple memory types: in-context working memory (current context), external episodic memory (vector database of past actions), and semantic memory (knowledge about the world and available tools).
Multi-Agent Systems
The most capable deployments use networks of specialized agents rather than a single generalist agent. A coordinator agent decomposes complex goals into sub-tasks, dispatches them to specialized agents (researcher, coder, writer, evaluator), and synthesizes their outputs.