AI Agents Glossary

Name: AgentsX
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A comprehensive guide to understanding AI agents, agentic AI systems, and autonomous artificial intelligence terminology.

A

Agent: An autonomous software entity that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human supervision.
Agentic AI: Artificial intelligence systems that exhibit agency - the ability to act independently, make autonomous decisions, pursue goals, and adapt behavior based on environmental feedback.
AGI (Artificial General Intelligence): Hypothetical AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or beyond human capability, rather than being specialized in narrow domains.
Agent Orchestration: The coordination and management of multiple AI agents working together, including task distribution, communication protocols, and workflow synchronization.
Autonomy: The degree to which an AI system can operate independently without human intervention, making decisions and taking actions based on its programming and learned experience.
Action: A specific operation or task that an AI agent executes to interact with its environment. Actions can range from simple function calls to complex multi-step operations involving multiple tools. An action represents the 'Act' phase in the Think-Act-Observe agent cycle.
Agent Framework: Software infrastructure that provides building blocks for creating AI agents, including tool integration, reasoning engines, memory systems, and orchestration capabilities. Examples include LangChain, AutoGPT, and smolagents.
Agent Loop: The continuous cycle of perception, reasoning, action, and observation that characterizes agent behavior. The agent loop continues until the task is complete or a termination condition is met.

C

Cognitive Architecture: The underlying structure that defines how an AI agent processes information, makes decisions, stores knowledge, and learns from experience.
Chain-of-Thought: A reasoning technique where AI agents break down complex problems into intermediate steps, making their decision-making process more transparent and reliable.
Collaborative Agents: Multiple AI agents that work together, sharing information and coordinating actions to achieve common goals more effectively than individual agents could alone.

D

Decision-Making: The process by which an AI agent selects actions from available options based on its goals, current state, and learned policies. Effective decision-making is central to agent autonomy.
Deliberative Agent: An AI agent that uses internal models and planning to reason about future states and consequences before taking action, as opposed to purely reactive behavior.

E

Emergent Behavior: Complex patterns or capabilities that arise from the interaction of multiple simple agents, not explicitly programmed into any individual agent.
Environment Perception: An agent's ability to sense and interpret data from its operational environment, including inputs from APIs, databases, sensors, and other information sources.

F

Function Calling: The capability of AI agents to invoke specific functions or tools by generating structured code or API calls. Function calling enables agents to extend their abilities beyond text generation.
Frontier Model: The most advanced and capable AI models at any given time, typically large language models at the cutting edge of performance. AgentsX uses multiple specialized models rather than relying solely on frontier models.

G

Goal-Oriented Behavior: The characteristic of AI agents to autonomously plan and execute actions specifically designed to achieve defined objectives or outcomes.
Guardrails: Safety mechanisms and constraints that limit an AI agent's actions to prevent unintended consequences, ensure compliance, and maintain security.

I

Instruction Following: The ability of AI agents to understand and execute complex natural language instructions, breaking them down into actionable steps.
Iterative Refinement: The process by which agents improve their outputs through multiple rounds of generation, evaluation, and modification based on feedback.

L

LLM (Large Language Model): Neural networks trained on vast amounts of text data, capable of understanding and generating human-like text, often serving as the reasoning engine for AI agents.
Learning Agent: An AI agent that improves its performance over time through experience, feedback, and interaction with its environment.

M

Multi-Agent System: A system composed of multiple interacting AI agents, often with specialized roles, working together to solve complex problems through distributed intelligence.
Mixture of Experts (MoE): An architecture where multiple specialized AI models or agents are combined, with a gating mechanism determining which expert(s) handle each specific task or input.
Memory System: The component that allows AI agents to store and recall information from past interactions, enabling learning and context-aware decision-making.

N

Natural Language Understanding (NLU): The capability of AI systems to comprehend human language input, extract meaning, and identify intent—essential for agents that interact through conversation.
Neural Architecture: The design and structure of neural networks that power AI agents, including transformer architectures commonly used in modern language models.

O

Observation: The feedback an agent receives after taking an action, which informs future decisions. Observation completes the Think-Act-Observe cycle central to agent behavior.
Orchestration Layer: The system component responsible for coordinating multiple agents, managing task distribution, handling communication, and ensuring workflow coherence.

P

Planning: The process by which an AI agent determines the sequence of actions needed to achieve its goals, considering current state, available resources, and potential obstacles.
Prompt Engineering: The practice of crafting effective instructions and context for AI agents to optimize their performance and output quality.

R

Reasoning: The cognitive process by which AI agents draw conclusions, make inferences, and solve problems based on available information and learned knowledge.
Reactive Agent: An AI agent that responds directly to current environmental inputs without maintaining internal state or considering historical context.
Reinforcement Learning: A machine learning approach where agents learn optimal behaviors through trial and error, receiving rewards or penalties based on action outcomes.

S

Situated Agent: An AI agent that operates within a specific environment or context, perceiving and acting based on its particular situation rather than in abstract isolation.
State Management: How an agent tracks and maintains information about its current situation, including conversation history, task progress, and environmental variables.
System Prompt: Initial instructions that define an agent's role, capabilities, constraints, and behavioral guidelines. The system prompt shapes how the agent interprets and responds to requests.

T

Tool Use: The ability of AI agents to interact with external tools, APIs, databases, and software systems to accomplish tasks beyond their core capabilities.
Task Decomposition: Breaking down complex objectives into smaller, manageable sub-tasks that can be executed sequentially or in parallel by one or more agents.
Think-Act-Observe: The fundamental cycle of agent operation: thinking (reasoning and planning), acting (executing selected actions using tools), and observing (receiving feedback to inform next steps).
Tool Integration: The process of connecting external functions, APIs, and capabilities to an AI agent, expanding what the agent can accomplish beyond its core model abilities.

V

Vision Language Model (VLM): AI models that can process and understand both images and text, enabling agents to work with visual information alongside natural language.
Virtual Assistant: An AI agent designed to help users with tasks through natural language interaction, often with access to multiple tools and information sources. Examples include Siri, Alexa, and custom enterprise assistants.

W

Workflow Automation: The use of AI agents to automatically execute multi-step business processes, reducing manual effort and improving efficiency and consistency.
Workflow Orchestration: Coordinating and managing the execution of complex workflows involving multiple agents, tools, and decision points.

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AI Agents Glossary

A comprehensive guide to understanding AI agents, agentic AI systems, and autonomous artificial intelligence terminology.

A

Agent: An autonomous software entity that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human supervision.
Agentic AI: Artificial intelligence systems that exhibit agency - the ability to act independently, make autonomous decisions, pursue goals, and adapt behavior based on environmental feedback.
AGI (Artificial General Intelligence): Hypothetical AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or beyond human capability, rather than being specialized in narrow domains.
Agent Orchestration: The coordination and management of multiple AI agents working together, including task distribution, communication protocols, and workflow synchronization.
Autonomy: The degree to which an AI system can operate independently without human intervention, making decisions and taking actions based on its programming and learned experience.
Action: A specific operation or task that an AI agent executes to interact with its environment. Actions can range from simple function calls to complex multi-step operations involving multiple tools. An action represents the 'Act' phase in the Think-Act-Observe agent cycle.
Agent Framework: Software infrastructure that provides building blocks for creating AI agents, including tool integration, reasoning engines, memory systems, and orchestration capabilities. Examples include LangChain, AutoGPT, and smolagents.
Agent Loop: The continuous cycle of perception, reasoning, action, and observation that characterizes agent behavior. The agent loop continues until the task is complete or a termination condition is met.

C

Cognitive Architecture: The underlying structure that defines how an AI agent processes information, makes decisions, stores knowledge, and learns from experience.
Chain-of-Thought: A reasoning technique where AI agents break down complex problems into intermediate steps, making their decision-making process more transparent and reliable.
Collaborative Agents: Multiple AI agents that work together, sharing information and coordinating actions to achieve common goals more effectively than individual agents could alone.

D

Decision-Making: The process by which an AI agent selects actions from available options based on its goals, current state, and learned policies. Effective decision-making is central to agent autonomy.
Deliberative Agent: An AI agent that uses internal models and planning to reason about future states and consequences before taking action, as opposed to purely reactive behavior.

E

Emergent Behavior: Complex patterns or capabilities that arise from the interaction of multiple simple agents, not explicitly programmed into any individual agent.
Environment Perception: An agent's ability to sense and interpret data from its operational environment, including inputs from APIs, databases, sensors, and other information sources.

F

Function Calling: The capability of AI agents to invoke specific functions or tools by generating structured code or API calls. Function calling enables agents to extend their abilities beyond text generation.
Frontier Model: The most advanced and capable AI models at any given time, typically large language models at the cutting edge of performance. AgentsX uses multiple specialized models rather than relying solely on frontier models.

G

Goal-Oriented Behavior: The characteristic of AI agents to autonomously plan and execute actions specifically designed to achieve defined objectives or outcomes.
Guardrails: Safety mechanisms and constraints that limit an AI agent's actions to prevent unintended consequences, ensure compliance, and maintain security.

I

Instruction Following: The ability of AI agents to understand and execute complex natural language instructions, breaking them down into actionable steps.
Iterative Refinement: The process by which agents improve their outputs through multiple rounds of generation, evaluation, and modification based on feedback.

L

LLM (Large Language Model): Neural networks trained on vast amounts of text data, capable of understanding and generating human-like text, often serving as the reasoning engine for AI agents.
Learning Agent: An AI agent that improves its performance over time through experience, feedback, and interaction with its environment.

M

Multi-Agent System: A system composed of multiple interacting AI agents, often with specialized roles, working together to solve complex problems through distributed intelligence.
Mixture of Experts (MoE): An architecture where multiple specialized AI models or agents are combined, with a gating mechanism determining which expert(s) handle each specific task or input.
Memory System: The component that allows AI agents to store and recall information from past interactions, enabling learning and context-aware decision-making.

N

Natural Language Understanding (NLU): The capability of AI systems to comprehend human language input, extract meaning, and identify intent—essential for agents that interact through conversation.
Neural Architecture: The design and structure of neural networks that power AI agents, including transformer architectures commonly used in modern language models.

O

Observation: The feedback an agent receives after taking an action, which informs future decisions. Observation completes the Think-Act-Observe cycle central to agent behavior.
Orchestration Layer: The system component responsible for coordinating multiple agents, managing task distribution, handling communication, and ensuring workflow coherence.

P

Planning: The process by which an AI agent determines the sequence of actions needed to achieve its goals, considering current state, available resources, and potential obstacles.
Prompt Engineering: The practice of crafting effective instructions and context for AI agents to optimize their performance and output quality.

R

Reasoning: The cognitive process by which AI agents draw conclusions, make inferences, and solve problems based on available information and learned knowledge.
Reactive Agent: An AI agent that responds directly to current environmental inputs without maintaining internal state or considering historical context.
Reinforcement Learning: A machine learning approach where agents learn optimal behaviors through trial and error, receiving rewards or penalties based on action outcomes.

S

Situated Agent: An AI agent that operates within a specific environment or context, perceiving and acting based on its particular situation rather than in abstract isolation.
State Management: How an agent tracks and maintains information about its current situation, including conversation history, task progress, and environmental variables.
System Prompt: Initial instructions that define an agent's role, capabilities, constraints, and behavioral guidelines. The system prompt shapes how the agent interprets and responds to requests.

T

Tool Use: The ability of AI agents to interact with external tools, APIs, databases, and software systems to accomplish tasks beyond their core capabilities.
Task Decomposition: Breaking down complex objectives into smaller, manageable sub-tasks that can be executed sequentially or in parallel by one or more agents.
Think-Act-Observe: The fundamental cycle of agent operation: thinking (reasoning and planning), acting (executing selected actions using tools), and observing (receiving feedback to inform next steps).
Tool Integration: The process of connecting external functions, APIs, and capabilities to an AI agent, expanding what the agent can accomplish beyond its core model abilities.

V

Vision Language Model (VLM): AI models that can process and understand both images and text, enabling agents to work with visual information alongside natural language.
Virtual Assistant: An AI agent designed to help users with tasks through natural language interaction, often with access to multiple tools and information sources. Examples include Siri, Alexa, and custom enterprise assistants.

W

Workflow Automation: The use of AI agents to automatically execute multi-step business processes, reducing manual effort and improving efficiency and consistency.
Workflow Orchestration: Coordinating and managing the execution of complex workflows involving multiple agents, tools, and decision points.

Quick Navigation

A C D E F G I L M N O P R S T V W