25 Agentic AI Terms Every Business User Needs To Know

25 Agentic AI Terms Every Business User Needs To Know

For AI to be successfully adopted across an organization, everyone needs a fundamental understanding of how it works.

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Everyone's talking about agentic AI and its promise of transformative efficiency and unprecedented automation. But the terminology is piling up faster than your unread Slack messages. If you don't keep up, it's easy to fall behind.

Don't let confusion over agentic artificial intelligence (AI) lingo be a roadblock to adoption: This glossary will help you understand the basics so you can feel confident about your adoption strategy. As AI is extraordinarily dynamic, it will be updated as new concepts and terms arise.

Agentic AI

A type of AI that uses AI agents to get work done autonomously.

AI agent

A piece of software that uses generative AI large language models (LLMs) to make decisions about what to do and how to do it. AI agents use machine learning and natural language processing (NLP) to handle everything from answering simple questions to resolving complex issues.

Agent-to-human handoff

The transfer of a customer interaction from an AI agent to a human rep. The most successful handoffs make sure that context, conversation history, and all relevant data are transferred so customers don't have to repeat themselves.

Agentic workflow

An AI-driven process that uses one or more agents to get work done.

Complexity threshold

The point at which a task or problem exceeds the capabilities of an AI system, and requires either more advanced AI, or a handoff to a human worker to complete the task or answer the question.

Context awareness

Context-aware AI uses past interactions and real-time data to understand and respond to a user's unique environment and situation (such as time of day, or whether they're at home or work). This helps the AI agent give more relevant and personalized responses.

Context window

A context window is an AI model's short-term memory. It's the amount of text (measured in tokens, see definition below) that the model can read and consider at one time when generating a response. If the text goes beyond this time limit, the model starts to forget or drop the earliest parts as it processes the newer ones.

Deterministic reasoning

A type of reasoning that exclusively uses rule-based logic to guarantee the same output for the same input. AI agents are non-deterministic, meaning they can produce different outputs or take different actions even when given the same prompts. Salesforce Flow, a tool for automating complex workflows, uses deterministic reasoning.

Digital labor

Digital labor refers to technologies (such as AI automation and AI agents) that mimic human decision-making and cognitive abilities. It extends human capacity to complete tasks at speeds and scales that a human-only workforce cannot match.

Digital worker

A digital worker is an AI software application that mimics human capabilities and handles complex tasks. They are AI agents that function as virtual employees and can perform various roles that previously could only be accomplished by human workers.

Dynamic resource allocation

A technique of distributing resources like compute power and memory where they're needed most, in real time. Instead of using the same amount of resources for every task, AI systems can adjust on the fly, giving more power to high-priority tasks while using less power for lower-priority ones. This makes AI more efficient, faster, and able to handle complex tasks without wasting energy or slowing down.

Explainability and transparency

Explainability is the AI system's ability to articulate why it makes the decisions and outputs that it does, clearly communicating the reasoning behind its outputs. It's the why. Transparency is its ability to provide users with clear insights into its processes and data sources. It's the how.

Headless AI agent

An AI system that operates without a traditional user interface. It functions entirely in the background to execute tasks and make decisions without human interaction. Instead of integrating with a chatbot or dashboard, the AI agent works with existing systems via APIs, automating workflows and responding to triggers in real time.

Intent recognition

AI's ability to understand the purpose or goal behind a user's input. For example, in customer support, intent recognition can distinguish whether a customer is asking about billing issues, product help, or order status, which helps the AI agent give the best response.

Long-term coherence

The ability of AI agents to maintain consistency and context across extended interactions and workflows. For example, an agent that helps manage software subscriptions would recall that a customer inquired about renewing months ago, but never followed up. With long-term coherence, the AI recalls all past interactions, and proactively suggests relevant options.

Multi-agent system

A system where multiple AI agents work together to handle complex tasks. Instead of one agent operating alone, a multi-agent system coordinates across different business areas to automate and make decisions more intelligently and efficiently.

Multimodal AI

These systems process and act on different inputs like text, images, audio, and video. A multimodal AI agent could analyze a voice command, interpret an image, and read a document simultaneously to provide better responses and resolve issues without human intervention.

Ontology

A system for organizing information so computers can understand and use that data to make decisions on the output. Ontologies form data models for knowledge graphs, ensuring consistency and understanding.

Polyphonic AI

A system where specialized AI agents, each with its own expertise, work together. For example, a service agent receives a return request, while an inventory agent checks availability and a logistics agent works out shipping details. An orchestrator agent pulls all of this together into one smooth interaction.

Probabilistic reasoning

A type of reasoning that draws conclusions and makes predictions based on probabilities that estimate the likelihood of different outcomes, allowing some degree of uncertainty and variation. Some variances in responses are acceptable, which allows the AI agent to make predictions even if information is incomplete or ambiguous. For example, probabilistic reasoning infers the most likely intent of a question like, "would this solution work for a mid-size team?" without knowing the industry or use case.

Reasoning engine

A reasoning engine is an AI system that mimics human-like decision-making and problem-solving based on certain rules, data, and logic. It's how an agent decides what actions to take, and which data is needed to take those actions. The system emulates three types of human reasoning: deductive (reaching conclusions from facts), inductive (reaching a likely conclusion from patterns), and abductive (its best guess).

Reinforcement learning

A type of machine learning where an AI agent learns by interacting with an environment, taking actions, and receiving feedback about the completeness and accuracy of its outputs. Over time, the agent improves its decision-making to maximize consistently positive outcomes.

Sentiment analysis

AI's ability to detect and interpret the emotional tone behind a customer's words, helping it understand whether the sentiment is positive (like enthusiasm or satisfaction), negative (such as frustration or urgency), or neutral. It looks for clues in language, punctuation, and even emojis to gauge how someone is feeling.

Token

A token is a bit of text, a word, part of a word, or punctuation, that an AI model uses to process language. For example, "cat" is one token, but "unbelievable" might be split into several tokens like "un," "believe," and "able." Models read and generate text in tokens, not full words, which is why token limits matter for things like context windows.

Utterance

A single input a user provides to an AI agent, like a command or question. It's the specific information the agent analyzes to understand what the user needs, so it can respond or take action.

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Lisa Lee is a contributing editor at Salesforce. She has written about technology and its impact on business for more than 25 years. Prior to Salesforce, she was an award-winning journalist with Forbes.com and other publications.

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