AI Agents explained in 3 steps

AI agents represent a shift from static LLM prompts to autonomous systems capable of goal-oriented task execution. The architecture begins with the perception layer, where multimodal inputs are ingested and contextualized. The reasoning phase leverages the LLM's internal logic to decompose complex objectives into actionable sub-tasks, often utilizing frameworks like ReAct to interleave thought processes with tool invocations. Finally, the execution layer bridges the gap between digital reasoning and physical or software-based actions. By utilizing function calling and API integrations, agents can manipulate external environments, retrieve real-time data, or execute code. This iterative loop of sensing, thinking, and acting allows for self-correction and dynamic adaptation to changing states, distinguishing agents from standard chatbots.