60 AI Agents Terms You Must Know in 2025

Artificial intelligence (AI) agents are at the heart of many groundbreaking AI applications. Unlike general-purpose AI systems, AI agents are self-governing entities designed to perceive their environment, make decisions, and execute actions to achieve specific goals. Their applications range from virtual assistants scheduling appointments to advanced robotics optimizing supply chains. These agents operate autonomously, intelligently, and adapt to changing conditions, revolutionizing various industries.

The expanding field of AI agents necessitates a growing vocabulary. Terms like "Tool Use" and "Reflection," once confined to academic circles, are now crucial for developers, executives, and AI enthusiasts. Understanding this specialized terminology is vital for grasping the capabilities, limitations, and ethical implications of AI agents, whether you're studying reinforcement learning (RL) or multi-agent systems (MAS). This guide outlines 60 essential AI agent terms you need to know.

What are AI Agents?

AI agents are autonomous systems built to interact with their environment and complete tasks independently. They begin by perceiving inputs (user queries, data, etc.), processing this information to determine the best course of action. Unlike traditional AI models reliant on pre-programmed rules or static datasets, intelligent agents adapt in real-time, learn from new data, and make decisions based on evolving circumstances. The following section details over 50 key AI agent terms.

AI Agent Terminology

Here's a glossary of essential AI agent terms:

1. AI Agent

An AI agent is a digital assistant or robot that perceives its environment, processes information, and takes actions to achieve goals. A self-driving car, using cameras and sensors to navigate roads, is a prime example.

2. Autonomous Agent

An autonomous agent operates independently without human oversight. A delivery drone selecting its route, avoiding obstacles, and completing delivery without assistance is an example.

3. Action

Actions are the steps an AI agent takes to complete a task. A chatbot sending a reply or a robot grasping a tool are examples. These actions are determined by the agent's goals.

4. Actuators

Actuators are the components enabling an AI agent's movement. In a robot, this might be a motor moving its arm; in a virtual agent, it could be software commands sending emails or updating a database.

5. Agentic AI Design Patterns

Structured approaches for building AI agents capable of independent perception, thought, and action. These include deliberative agents (goal-oriented planning) and reactive agents (real-time responders). These patterns enhance adaptability and decision-making in dynamic environments.

6. Agentic RAG

Agentic Retrieval-Augmented Generation (RAG) enhances an AI's recall, reasoning, and self-answering capabilities. Unlike traditional RAG, agentic RAG iteratively refines its output through self-questioning, improving accuracy and self-correction. This significantly improves automated decision-making, research tools, and AI assistants.

7. Belief State

An AI agent's informed estimate of the current situation. If an agent's vision is blocked, it relies on existing data to infer what's happening and make decisions accordingly.

8. Chatbot

A conversational AI agent interacting with users. Chatbots use Natural Language Processing (NLP) to understand and respond naturally to human communication, handling tasks from ordering pizza to customer service.

9. Reflection

The mental process where an AI agent evaluates its performance ("Did I do that correctly?"). This self-assessment allows the agent to improve over time.

10. Tool Use

An AI agent's ability to utilize external resources. For example, using a calculator app to solve math problems or accessing a weather API for forecasts.

11. Multi-Agent System (MAS)

A collection of AI agents working together. In a warehouse, robots might sort packages while others move them, collaborating for efficient delivery.

12. Emergent Behavior

Complex outcomes arising from the interaction of simple AI agents. A flock of birds, each following basic rules, creates complex flight patterns.

13. Federated Learning

A collaborative learning process where AI agents share learned knowledge without directly sharing data, preserving privacy while improving overall performance.

14. Human-in-the-Loop (HITL)

A system where humans guide or correct an AI agent's actions, maintaining oversight and control.

15. Planning

An AI agent's process of anticipating future events, similar to planning a road trip – determining routes, potential stops, and contingencies.

16. Goal

The objective an AI agent strives to achieve, ranging from winning a game to retrieving information.

17. Utility Function

A metric measuring an AI agent's success, assigning values to outcomes to guide decision-making.

18. Heuristic

A shortcut used by AI agents to simplify problem-solving, employing rules of thumb rather than exhaustive analysis.

19. Knowledge Graph

A network of data used by an AI agent to understand relationships between information, similar to a mind map connecting ideas and facts.

20. Ontology

A structured vocabulary defining concepts and their relationships, helping an AI agent understand its environment.

21. Rule-Based System

A system where an AI agent makes decisions based on pre-defined rules, like "If light is red, stop."

22. World Model

An AI agent's internal representation of its environment, used for prediction and planning.

23. Model Drift

Deterioration in an AI agent's performance due to changes in its environment (like using an outdated map).

24. Ethical AI

Creating fair, unbiased, and transparent AI agents that respect human values and avoid harm.

25. Algorithmic Fairness

Ensuring AI agents treat all individuals equally, avoiding biases that favor certain groups.

26. Swarm Intelligence

Collective problem-solving by groups of AI agents, mimicking the behavior of insect colonies.

27. Transfer of Control

A human taking over decision-making from an AI agent, like a pilot overriding autopilot.

28. Fail-Safe Mechanism

A system ensuring an AI agent handles errors gracefully, providing a safety net for unexpected situations.

29. Human-Agent Collaboration

Humans and AI working together, like a chef and sous-chef collaborating in a kitchen.

30. Memory Modules

Systems for storing and retrieving information, enabling AI agents to learn from experience.

31. Hierarchical Multiagent System

Agents organized in a hierarchy, with higher-level agents coordinating lower-level agents.

32. Simple-Reflex Agent

An agent reacting directly to its current situation without considering past or future.

33. ReWOO (Reasoning WithOut Observations)

Reasoning and acting based on inferred information rather than direct observation.

34. ReAct (Reasoning and Acting)

An iterative process of reasoning and acting, refining actions based on outcomes.

35. Agentforce (Default)

A system managing multiple AI agents.

36. Brain of an AI – LLM (Large Language Model)

The core processing unit of an AI agent, handling input, generating responses, and making decisions.

37. Reference Actions

Example actions guiding an AI agent's decisions.

38. Standard Actions

Pre-defined actions an AI agent can perform.

39. Standard Topics

Subjects an AI agent is trained to handle.

40. System Actions

Background operations performed by an AI agent.

41. Knowledge Representation

How an AI agent stores and uses information.

42. Singularity

A hypothetical point where AI surpasses human intelligence.

43. LangChain

A framework for building AI agents using LLMs, integrating knowledge retrieval, APIs, and reasoning.

44. AutoGen

A framework for creating autonomous multi-agent systems.

45. SmolAgents

A lightweight framework for building AI agents.

46. CrewAI

A multi-agent orchestration framework.

47. Small Language Model

A compact LLM for resource-efficient AI agents.

48. Tokens

The smallest units of text processed by an AI agent.

49. Prompts

Instructions given to AI agents to elicit responses.

50. Context Window

The amount of past information an AI agent can access.

51. Hallucinations

Instances where an AI agent generates false information.

52. Temperatures

A parameter controlling the randomness of an AI agent's responses.

53. Chain-of-thought Prompting

A technique for guiding AI agents through step-by-step reasoning.

54. Function calling

Using functions to interact with external systems.

55. Agent Framework

Tools and infrastructure for building AI agents.

56. Agentic Workflow

A structured process for autonomous task completion by AI agents.

57. Langgraph

A framework for building stateful multi-agent LLM applications.

58. Long-term memory

Persistent storage for AI agents.

59. Short-term memory

Temporary storage for immediate context.

60. Reinforcement Learning (RL)

A machine learning technique where an AI agent learns through trial and error, receiving rewards for desired actions.

Conclusion

AI agent terminology is crucial for understanding how intelligent systems make autonomous decisions. From robots navigating physical spaces to chatbots enhancing online interactions, AI agents are transforming industries and problem-solving approaches. Ethical and transparent operation is paramount. Understanding these terms empowers us to shape the future of AI, ensuring these systems serve as partners in progress.

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