AI Agents
Autonomous systems that perceive, reason, and act.
Unlike standard AI models (like a chatbot that only replies when spoken to), AI Agents are goal-oriented and autonomous. They operate in loops: Observe -> Think -> Act. An agent can be given a high-level objective (e.g., 'Book a flight to London under $500'), and it will independently figure out the steps: checking dates, comparing prices, and navigating websites, adapting its plan if it encounters errors.
graph LR
Center["AI Agents"]:::main
Pre_artificial_intelligence["artificial-intelligence"]:::pre --> Center
click Pre_artificial_intelligence "/terms/artificial-intelligence"
Pre_reinforcement_learning["reinforcement-learning"]:::pre --> Center
click Pre_reinforcement_learning "/terms/reinforcement-learning"
Pre_large_language_model["large-language-model"]:::pre --> Center
click Pre_large_language_model "/terms/large-language-model"
Rel_automation["automation"]:::related -.-> Center
click Rel_automation "/terms/automation"
Rel_agentic_ai["agentic-ai"]:::related -.-> Center
click Rel_agentic_ai "/terms/agentic-ai"
Rel_generative_ai_agents["generative-ai-agents"]:::related -.-> Center
click Rel_generative_ai_agents "/terms/generative-ai-agents"
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🧠 Knowledge Check
🧒 Explain Like I'm 5
🤖 AI agents are like super-smart helpers that can see, think, and do things all by themselves to get jobs done.
🤓 Expert Deep Dive
## Technical Analysis of "AI Agents"
AI agents represent sophisticated computational systems engineered for autonomous operation. At their core, they integrate three critical subsystems: Perception, Reasoning, and Action.
The Perception Subsystem is responsible for environmental sensing and interpretation. It leverages a diverse array of sensors, including cameras, LiDAR, microphones, and digital interfaces like APIs and databases. Data modalities span images, audio, text, and structured formats. Raw sensor data undergoes rigorous preprocessing and feature extraction, employing techniques such as deep learning embeddings, object detection, and speech-to-text, to generate a robust internal state representation. This representation can be symbolic, sub-symbolic, or probabilistic, enabling environment modeling and state estimation, potentially incorporating advanced concepts like Simultaneous Localization and Mapping (SLAM) for agents operating in dynamic environments.
The Reasoning Subsystem forms the agent's cognitive engine, processing perceptual inputs, internal knowledge, and defined goals to determine optimal actions. This involves sophisticated decision-making frameworks, ranging from rule-based systems and Finite State Machines to advanced probabilistic models like Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs). Planning algorithms such as A* and Monte Carlo Tree Search (MCTS) are often employed, alongside various Reinforcement Learning (RL) paradigms (value-based, policy-based, model-based) for learning optimal behaviors. Knowledge Representation and Reasoning (KRR) techniques, including knowledge graphs and ontologies, facilitate structured knowledge access and logical inference.
The Action Subsystem translates reasoned decisions into tangible environmental interactions. This is achieved through a variety of actuators and interfaces, encompassing physical mechanisms like robotic manipulators and digital means such as API calls and GUI automation. The action space can be discrete or continuous, requiring sophisticated action sequencing and, for physical agents, trajectory generation. Closed-loop control mechanisms, utilizing feedback loops and error detection/recovery protocols, ensure accurate and robust execution of actions, adapting to real-time environmental changes.
Agent architectures can vary from modular designs, allowing for component specialization, to end-to-end systems. The level of autonomy, robustness, and explainability are key design considerations, alongside potential interactions within Multi-Agent Systems (MAS), which necessitate protocols for coordination, communication, and negotiation.