"AI agents are redefining how AI operates in practice. Unlike static models, agents are autonomous systems that perceive their environment, make decisions, and act toward achieving specific goals. For data scientists, ML engineers, and analysts, understanding the terminology behind AI agents is critical. New frameworks, methods, and design patterns are shaping how autonomous systems are built and deployed across industries."
"Autonomous Agent An agent that operates without constant human oversight. Example: AutoGPT, which can plan and execute steps toward a user-defined objective. Environment The external world an agent interacts with, whether physical (streets, factories) or digital (databases, APIs). Action The step an agent takes to influence its environment - sending an email, moving a robot arm, or executing an API call."
AI agents are autonomous software or robotic entities that observe environments, reason, and act to achieve goals. Agents adapt dynamically rather than following fixed rules. Environments can be physical (streets, factories) or digital (databases, APIs). Actions are changes an agent makes, executed by actuators such as motors or function calls. Autonomous agents can operate without constant human oversight, exemplified by systems like AutoGPT that plan and execute multi-step objectives. New frameworks, methods, and design patterns guide how agents are defined, built, and deployed. Practitioners must learn core terminology and 30 key terms to remain current with agentic AI in 2025.
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