Reference
Plain-English definitions of the AI terms you keep hearing, what they mean, and when they matter inside a service business.
Software that uses AI to complete a goal through several steps. It can decide what to do next, use tools, check the result, and adjust its approach.
An agent can handle work that is too flexible for a fixed automation. That freedom also makes it harder to predict and test.
An agent reviews a new client request, checks the account in your CRM, drafts a response, and creates the next task.
A broad name for AI systems that can make decisions and take actions while working toward a goal.
Agentic describes how a system behaves. An AI agent is a system built to behave that way.
A business is AI-native when AI is part of how its work is designed and delivered from the beginning.
A firm becomes AI-native when it rebuilds roles, processes, quality control, and client delivery around what AI makes possible. Buying an AI tool alone does not do this.
A defined series of steps that includes one or more AI tasks.
A workflow follows a path designed in advance. It is usually easier to control than an agent that chooses its own path.
A workflow takes a sales call transcript, extracts the client’s needs, drafts a proposal, sends it for approval, and saves the approved version.
Software that performs a repeatable task according to set rules.
Traditional automation works well when the steps and inputs are predictable. AI extends automation to work that involves language, judgment, or messy information.
An AI assistant that helps a person perform work but leaves that person in control.
A copilot may draft, summarize, or suggest. It normally waits for a person to review its work or choose the next step.
The type of AI behind tools such as ChatGPT, Claude, and Gemini. It learns patterns from large amounts of information and uses them to produce a response.
An LLM can work with language, but it does not know facts in the same way a person does. Its answers still need the right context and checks.
The underlying AI system that processes a request and produces a result.
Models vary in quality, speed, cost, and the work they can handle. The most capable model is not always the best business choice.
The instructions and information given to an AI model.
Clear instructions help, but a clever prompt cannot repair a poorly designed process or missing business information.
The information available to an AI while it handles a request.
Context can include instructions, earlier messages, client details, policies, examples, and documents. The right context usually matters more than more context.
The amount of information a model can consider during one interaction.
A larger context window allows longer documents and conversations. It does not guarantee that the model will use every detail correctly.
The work of deciding what instructions, data, examples, tools, and history an AI needs to complete a task well.
Good context engineering gives the AI the same operating information a capable employee would need.
A function an AI agent can use to retrieve information or take an action.
Tools can search, read a database, update a CRM, or send a message. Giving AI a tool is similar to giving an employee access to a business system, so permissions matter.
A reusable set of instructions that teaches an AI how to perform a particular kind of work.
A tool gives AI an ability. A skill explains how your business expects that ability to be used.
A proposal-writing skill can define what information to collect, which template to use, and how the final proposal should be checked.
A shared standard that helps AI applications connect to tools and information sources.
Think of MCP as a common type of connector. It can reduce the custom work needed to connect AI to documents, software, and company data.
A method that finds relevant information and gives it to an AI before the AI answers.
RAG can make answers more relevant and easier to verify. It does not guarantee that every answer will be accurate.
Before answering a staff question, the system searches your current operating procedures and supplies the relevant sections to the model.
Connecting an AI response to specific, trusted information.
A grounded answer can use your contracts, policies, service records, or approved research instead of relying only on patterns learned during training.
An answer that sounds believable but contains invented or unsupported information.
Hallucinations are a normal risk with generative AI. Important outputs need trusted sources, clear limits, and checking.
Information an AI system keeps and can use in later interactions.
Useful memory needs rules about what should be saved, updated, protected, and forgotten.
AI that can work with more than one kind of information, such as text, images, audio, and video.
This allows one system to work across the different formats already used in client delivery.
A multimodal system reads an inspection report, examines site photos, and uses both to draft a recommendation.
A model designed to spend more effort working through difficult problems before answering.
Reasoning models can help with planning and multi-step decisions. They are often slower and more expensive, so use them where better judgment has real value.
A small unit of text or other information that an AI model processes.
AI providers often use tokens to measure usage and cost. Longer instructions, documents, and answers usually consume more tokens.
Additional training that adapts a model to a particular kind of task or output.
Fine-tuning can help when a firm has many strong examples and needs consistent behavior at scale. Better instructions, context, or retrieval are often enough.
A repeatable test used to measure how well an AI system performs a task.
Evals turn “this seems to work” into something a firm can measure before clients or staff depend on it.
An eval checks whether every proposal uses the approved price and avoids unsupported claims.
A rule or control that limits what an AI system can do.
Guardrails can block sensitive data, limit access, or require approval before the system takes an important action.
A process in which a person reviews, approves, corrects, or takes over part of the AI’s work.
Human review belongs where a mistake carries meaningful cost. It does not need to slow down every small action.
The coordination of models, agents, tools, people, and workflows.
Orchestration decides what runs, in what order, and what happens when something fails or needs approval.
AI that operates software through the screen, keyboard, and mouse much like a person would.
Computer use can help with older systems that lack useful connections. It is usually less predictable than using an API.
A structured way for one piece of software to communicate with another.
An API can let an AI system read a CRM record, create an invoice, or update a project. It is usually more reliable than clicking through a screen.
