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AI agents are programs that perceive their environment, plan a sequence of steps, use tools to act, and loop back until a goal is achieved — unlike a one-shot LLM call that just predicts the next token.
AI agents think through a repeating observe-think-act loop, using chain-of-thought reasoning to plan which tool to call next and memory systems to avoid starting from scratch each step.
Memory is the hardest part of agent design. In-context memory fills up fast; vector stores add retrieval latency; episodic logs prevent repeated mistakes. Getting these layers right is the difference between a 5-step demo and a 100-step production agent.