{"id":9506,"date":"2025-05-30T09:43:00","date_gmt":"2025-05-30T16:43:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=9506"},"modified":"2025-12-19T17:38:30","modified_gmt":"2025-12-19T09:38:30","slug":"agentic-ai-definitions","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/ap\/blog\/agentic-ai-definitions\/","title":{"rendered":"23 Agentic AI Terms Every Business User Needs To Know"},"content":{"rendered":"\n

Everyone’s talking about agentic AI<\/a> and its promise of transformative efficiency and unprecedented automation. But the terminology is piling up faster than your unread Slack messages. If you don\u2019t keep up, it\u2019s easy to fall behind.<\/p>\n\n\n\n

Don\u2019t let confusion over agentic artificial intelligence (AI) <\/a>lingo be a roadblock to adoption: This glossary will help you understand the basics so you can feel confident about your adoption strategy.\u00a0As AI is extraordinarily dynamic, it will be updated as new concepts and terms arise.
<\/p>\n\n\n\n

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\n

Agentic AI<\/a>
AI agent<\/a>
Agent-to-human handoff<\/a>
Agentic workflow<\/a>
Complexity threshold<\/a>
Context awareness<\/a>
Context window<\/a>
Deterministic reasoning<\/a>
Digital labor<\/a>
Digital worker<\/a>
Dynamic resource allocation<\/a>
Explainability and transparency<\/a><\/p>\n\n\n\n

<\/p>\n<\/div>\n\n\n\n

\n

Headless AI agent<\/a>
Intent recognition<\/a>
Long-term coherence<\/a>
Multi-agent system<\/a>
Multimodal AI<\/a>
Ontology<\/a>
Polyphonic AI<\/a>
Probabilistic reasoning<\/a>
Reasoning engine<\/a>
Reinforcement learning<\/a>
Sentiment analysis<\/a>
Token<\/a>
Utterance<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

Agentic AI <\/h3>\n\n\n\n

A type of AI that uses AI agents<\/a> to get work done autonomously.\u00a0<\/p>\n\n\n\n

AI agent<\/h3>\n\n\n\n

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

Agent-to-human handoff<\/h3>\n\n\n\n

The transfer of a customer interaction from an AI agent to a human rep. The most successful handoffs<\/a> make sure that context, conversation history, and all relevant data are transferred so customers don\u2019t have to repeat themselves.\u00a0
<\/p>\n\n\n\n

Agentic workflow <\/h3>\n\n\n\n

An AI-driven process that uses one or more agents to get work done.
<\/p>\n\n\n\n

Complexity threshold<\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Context awareness <\/h3>\n\n\n\n

Context-aware AI uses past interactions and real-time data to understand and respond to a user\u2019s unique environment and situation (such as time of day, or whether they\u2019re at home or work). This helps the AI agent give more relevant and personalised responses.\u00a0<\/p>\n\n\n\n

Context window<\/h3>\n\n\n\n

A context window is an AI model\u2019s short-term memory. It\u2019s 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.<\/p>\n\n\n\n

Deterministic reasoning<\/h3>\n\n\n\n

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>, a tool for automating complex workflows, uses deterministic reasoning.\u00a0<\/p>\n\n\n\n

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D-I-Why? Deploy AI agents faster with Agentforce<\/strong><\/h2>\n\t\t\t\t\t\t\t

Building and deploying autonomous AI agents takes time. Agentforce, the agentic layer of the Salesforce platform, can reduce time to market by 16x compared to DIY approaches \u2014 with 70% greater accuracy, according to a new Valoir report.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t

\n\t\t\t\t\tRead the report<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t
\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\t\n\t\t\t\n\t\t\"\"\n\n\t\t\n\t\t\t\t\t\"\"\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t\"\"\n\t\t\n\t<\/div>\n\n\n\n

Digital labour<\/h3>\n\n\n\n

Digital labour<\/a> refers to technologies (such as\u00a0AI automation<\/ins><\/a>\u00a0and 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.<\/p>\n\n\n\n

Digital worker<\/h3>\n\n\n\n

A digital worker is an AI software application that mimics human capabilities and handles complex tasks. They are\u00a0AI agents<\/ins><\/a>\u00a0that function as virtual employees and can perform various roles that previously could only be accomplished by human workers.<\/p>\n\n\n\n

Dynamic resource allocation<\/h3>\n\n\n\n

A technique of distributing resources like compute power and memory where they\u2019re 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.
<\/p>\n\n\n\n

Explainability and transparency<\/h3>\n\n\n\n

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<\/em>. Transparency<\/a> is its ability to provide users with clear insights into its processes and data sources. It’s the how<\/em>.<\/p>\n\n\n\n

Headless AI agent<\/h3>\n\n\n\n

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.
<\/p>\n\n\n\n

Intent recognition<\/h3>\n\n\n\n

AI\u2019s ability to understand the purpose or goal behind a user\u2019s 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. 
<\/p>\n\n\n\n

Long-term coherence<\/h3>\n\n\n\n

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. <\/p>\n\n\n\n

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\n\t\t\t\tMore about AI agents\t<\/h2>\n<\/div>\n\n\n\n
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\n\t\t\t\t\"5<\/span>\t\t\t<\/figure>\n\t\t\t\t

\n\t\t\t5 Characteristics of an Agentforce Company: The Agent-First Blueprint\t\t<\/h4>\n\t<\/a>\n<\/div>\n\n<\/div>\n\n\n\n
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\n\t\t\t\t\"Illustration<\/span>\t\t\t<\/figure>\n\t\t\t\t

\n\t\t\tBehind The Scenes: Building Agentforce for Salesforce\t\t<\/h4>\n\t<\/a>\n<\/div>\n\n\n\n
\n\t\n\t\t\t\t\t
\n\t\t\t\t\"Screenshot<\/span>\t\t\t<\/figure>\n\t\t\t\t

\n\t\t\tThe Agentic Enterprise Starts With What You Already Have\t\t<\/h4>\n\t<\/a>\n<\/div>\n\n\n\n
\n\t\n\t\t\t\t\t
\n\t\t\t\t\"future<\/span>\t\t\t<\/figure>\n\t\t\t\t

\n\t\t\tThink AI Agents Are Impressive Now? Just Wait\t\t<\/h4>\n\t<\/a>\n<\/div>\n\n<\/div>\n<\/div>\n\n\t<\/div>\n<\/div>\n\n\n\n

Multi-agent system<\/h3>\n\n\n\n

A system where multiple AI agents work together to handle complex tasks. Instead of one agent operating alone, a multi-agent system<\/a> <\/strong>coordinates across different business areas to automate and make\u00a0 decisions more intelligently and efficiently.
<\/p>\n\n\n\n

Multimodal AI <\/h3>\n\n\n\n

These systems process and act on different inputs like text, images, audio, and video. A multimodal AI agent could analyse a voice command, interpret an image, and read a document simultaneously to provide better responses and resolve issues without human intervention.\u00a0<\/p>\n\n\n\n

Ontology<\/h3>\n\n\n\n

A system for organising 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.<\/p>\n\n\n\n

Polyphonic AI <\/h3>\n\n\n\n

A system where specialised 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.
<\/p>\n\n\n\n

Probabilistic reasoning<\/h3>\n\n\n\n

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. <\/strong>For example, probabilistic reasoning infers the most likely intent of a question like, \u201cwould this solution work for a mid-size team?\u201d without knowing the industry or use case. <\/p>\n\n\n\n

Reasoning engine<\/h3>\n\n\n\n

A reasoning engine<\/a> is an AI system that mimics human-like decision-making and problem-solving based on certain rules, data, and logic. It\u2019s 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).\u00a0<\/p>\n\n\n\n

Reinforcement learning<\/h3>\n\n\n\n

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 maximise consistently positive outcomes.<\/p>\n\n\n\n

Sentiment analysis<\/h3>\n\n\n\n

AI\u2019s ability to detect and interpret the emotional tone behind a customer\u2019s 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.<\/p>\n\n\n\n

Token<\/h3>\n\n\n\n

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, \u201ccat\u201d is one token, but \u201cunbelievable\u201d might be split into several tokens like \u201cun,\u201d \u201cbelieve,\u201d and \u201cable.\u201d Models read and generate text in tokens, not full words, which is why token limits matter for things like context windows.<\/p>\n\n\n\n

Utterance <\/h3>\n\n\n\n

A single input a user provides to an AI agent, like a command or question. It’s the specific information the agent analyses to understand what the user needs, so it can respond or take action.<\/p>\n\n\n\n


<\/p>\n\n\n\n


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<\/p>\n","protected":false},"excerpt":{"rendered":"

For AI to be successfully adopted across an organisation, everyone needs a fundamental understanding of how it works. <\/p>\n","protected":false},"author":145,"featured_media":9505,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"sf_justforyou_enable_alt":true,"optimizely_content_id":"a773e3979b3647a498a2408fAP","post_meta_title":"","ai_synopsis":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"sf_topic":[482,23,665],"sf_content_type":[],"coauthors":[307],"class_list":["post-9506","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","sf_topic-generative-ai","sf_topic-artificial-intelligence","sf_topic-agentforce"],"yoast_head":"\n23 Agentic AI Definitions for Business Users - Salesforce<\/title>\n<meta name=\"description\" content=\"AI agent terminology is piling up faster than your unread Slack messages. 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