41 terms defined
AI Agent Glossary
AI terminology moves fast. This glossary keeps you current with clear definitions of every term that matters in 2026. Every term is defined in plain language, with a real example of how it works and why it matters. No PhD required.
Last reviewed and updated: August 2026
Core Concepts (8)
These are the foundational terms that everything else builds on. If you only learn ten terms, learn these.
Agentic AI
AI systems that can autonomously plan, reason, and take actions to achieve goals without continuous human guidance.
Read definition →AI Agent
A software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals.
Read definition →Autonomous Workflow
A business process that runs end-to-end with minimal or no human intervention.
Read definition →Natural Language Interface
Allows users to interact with software using everyday language instead of clicking buttons or writing code.
Read definition →Tool Use
The capability of an AI model to invoke external tools and APIs during a conversation.
Read definition →Agent Memory
Mechanisms that allow AI agents to retain and recall information across conversations and sessions.
Read definition →Prompt Engineering
The practice of designing and optimizing input instructions to get better outputs from AI models.
Read definition →Self-Healing Workflow
An automated process that detects failures and recovers without human intervention.
Read definition →Architecture and Patterns (8)
These terms describe how AI agents are structured and how they collaborate.
Multi-Agent System
Multiple AI agents working together, each with specialized roles, to accomplish complex tasks.
Read definition →Agent Orchestration
The coordination and management of multiple AI agents or agent components to complete complex workflows.
Read definition →ReAct (Reasoning + Acting)
An agent architecture pattern where the AI alternates between reasoning and acting.
Read definition →Chain-of-Thought (CoT)
A prompting technique that encourages the AI model to break down complex problems into intermediate reasoning steps.
Read definition →Plan-and-Execute
An agent architecture where the AI first creates a complete plan, then executes each step sequentially.
Read definition →Hierarchical Agents
A multi-level agent structure where a supervisor agent delegates tasks to sub-agents.
Read definition →Reflection
The agent's process of self-assessing its actions to improve future performance.
Read definition →Context Engineering
The practice of shaping what information an agent sees to optimize its output.
Read definition →AI Fundamentals (9)
These terms explain the underlying technology that powers AI agents.
Large Language Model (LLM)
A deep learning model trained on massive amounts of text data that can understand and generate human language.
Read definition →RAG (Retrieval-Augmented Generation)
A technique that enhances LLM responses by retrieving relevant information from external knowledge bases.
Read definition →Embeddings
Numerical vector representations of text in a high-dimensional space where semantically similar content is placed closer together.
Read definition →Fine-Tuning
Further training a pre-trained LLM on a specific, smaller dataset to adapt it for particular tasks or domains.
Read definition →Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated.
Read definition →Tokenization
The process of breaking text into smaller units called tokens for processing by LLMs.
Read definition →Context Window
The maximum amount of text an LLM can consider at one time, measured in tokens.
Read definition →Temperature
A parameter that controls how creative or random an AI's outputs are.
Read definition →Generative AI
AI systems that produce entirely new content based on patterns learned from data.
Read definition →Emerging Standards (3)
These are the new protocols and standards shaping how AI agents connect and collaborate.
MCP (Model Context Protocol)
An open standard developed by Anthropic that enables AI systems to securely connect to external data sources and tools.
Read definition →A2A (Agent-to-Agent Protocol)
An open protocol developed by Google that enables different AI agents to communicate and collaborate across platforms.
Read definition →Function Calling
A specific implementation of tool use where the AI model generates structured JSON output that maps to predefined function signatures.
Read definition →Automation Terminology (5)
These terms describe the automation landscape that AI agents are transforming.
RPA (Robotic Process Automation)
Software that mimics human actions to automate repetitive, rule-based tasks.
Read definition →Business Process Automation (BPA)
The use of technology to automate complex business processes end-to-end.
Read definition →Intelligent Automation
Combines RPA with AI technologies to handle both structured and unstructured data.
Read definition →Workflow Automation
The design, execution, and monitoring of structured business processes using software.
Read definition →AI Employee
An AI agent configured to perform the responsibilities of a specific business role.
Read definition →Enterprise and Governance (8)
These terms cover the security, compliance, and governance requirements for deploying AI agents in business environments.
Guardrails
Safety mechanisms that constrain AI agent behavior to prevent harmful, unintended, or out-of-scope actions.
Read definition →Human-in-the-Loop (HITL)
A setup where humans intervene or guide the agent's decision-making process at critical points.
Read definition →SOC 2 Compliance
A security framework that defines criteria for managing customer data based on five trust service criteria.
Read definition →Data Governance
Policies, processes, and controls that ensure data is accurate, consistent, secure, and used responsibly.
Read definition →AI Governance
Policies, processes, and controls that ensure AI systems are used responsibly, ethically, and in compliance.
Read definition →Grounding
Anchoring AI outputs to verified, specific source material to reduce hallucination.
Read definition →Bias in AI
When AI systems produce skewed or unfair outputs due to imbalances in training data.
Read definition →Explainability (XAI)
The ability to articulate how and why an AI system reached a particular output in human-understandable terms.
Read definition →No matching terms found. Try a different keyword.
What is the difference between an AI agent and a chatbot?
An AI agent performs multi-step workflows across your business tools. A chatbot answers questions. An agent can monitor Gmail, extract data, update your CRM, and send follow-up emails, all without human intervention. A chatbot tells you how to do those things. The difference is doing versus telling.
What is the most important AI term to learn in 2026?
Agentic AI. It represents the shift from AI as a chat tool to AI as a workforce. Understanding agentic AI means understanding how AI agents autonomously plan, reason, and execute business processes, which is the direction every major technology company is moving toward.
How does RAG help AI agents?
RAG (Retrieval-Augmented Generation) gives AI agents access to your actual business data, documents, databases, and knowledge bases. Instead of relying solely on training data, the agent retrieves relevant information before generating responses, which reduces hallucination and ensures answers reflect your real processes and data.
What is MCP and why does it matter?
MCP (Model Context Protocol) is an open standard developed by Anthropic that provides a universal way for AI agents to connect to external tools and data sources. Think of it as USB-C for AI. Instead of building custom integrations for every service, MCP provides one standard protocol that works across all compatible systems.
How do AI agents improve over time?
AI agents improve through agent memory (remembering past interactions), reflection (self-assessing performance), and learning from feedback. Shogo agents specifically learn from every interaction, adapting to your preferences, correcting mistakes, and getting better at handling your specific workflows.
What is the difference between RPA and AI agents?
RPA (Robotic Process Automation) follows predefined rules and cannot handle exceptions or adapt to new situations. AI agents can reason, make decisions, handle exceptions, and learn from outcomes. RPA is like a robot following a script. An AI agent is like a skilled employee who can think on their feet.
Are AI agents safe for business use?
AI agents are safe when deployed with proper guardrails, human-in-the-loop checkpoints, and data governance policies. Shogo maintains SOC 2 Type II compliance, implements role-based access controls, and provides audit logging for all agent actions. The key is deploying agents with the right constraints, not deploying them without constraints.
Sources
- McKinsey & Company. "Why agents are the next frontier of generative AI." McKinsey Digital, 2025.
- Gartner. "Top 10 Strategic Technology Trends for 2025." Gartner Newsroom, October 2024.
- IDC. "Worldwide AI Agent Forecast, 2024-2028." IDC Research, 2025.
- Anthropic. "Introducing Model Context Protocol." Anthropic News, November 2024.
- Google. "Introducing Agent2Agent Protocol." Google Developers Blog, April 2025.
- Stanford HAI. "AI Hallucinations: Risks and Challenges." Stanford University, 2025.
- Deloitte. "Automate to accelerate: The intelligent automation imperative." Deloitte Insights, 2025.
- IBM Research. "The enterprise AI agent landscape: 2026." IBM Think, 2026.
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