Reference
If an AI term keeps coming up and nobody has explained it clearly, start here. I explain what it means and when it matters in your business.
An AI agent is software that pursues a goal through several steps, picks an allowed next action, uses tools, and responds to each result.
An agent can help when you can't write every next step in advance. You still need to limit what it can do, check the result, and name who takes over if it fails.
An agent reads a new client request, checks the approved CRM account, drafts a reply, and records the next task.
Agentic AI describes AI systems that choose their next action and use tools while working toward a goal.
Agentic tells you that AI can choose its next step, but not how much you should let it do. You still need to set its limits and check its work.
A client-health system spots a stalled engagement, checks approved account history, picks an allowed action, and sends a risky case to a supervisor.
AI-native means a firm designs its work, delivery, and pricing around AI instead of adding AI to an older operating model.
Using AI every day can help, but AI-native means the way your firm delivers work has changed, including people's roles, quality checks, and pricing.
An AI-native firm prices a service by the outcome and capacity it can deliver, rather than by hours logged.
An AI workflow is a preplanned sequence that uses AI for one or more steps while keeping the order under the firm's control.
When you already know the steps, a workflow gives you a path you can test and follow when something breaks. You don't need an agent to choose steps you've already decided.
A workflow takes a sales transcript, extracts the request, drafts a proposal, sends it for approval, and files the approved version.
Automation is software that starts a repeatable task from a trigger and carries it through defined actions, checks, and exceptions with limited manual handling.
Automation can take repeated work off your team, but someone still needs to define how it starts, what finished means, and who takes over when it fails. Use AI where fixed rules struggle.
A signed agreement starts client onboarding, creates the account and project, copies the approved scope, assigns the delivery owner, and routes any duplicate or missing record to operations.
An AI copilot helps a person do a task while that person sets direction, checks the result, and decides what happens next.
A copilot can help your team research, draft, analyze, and answer clients faster, but someone still needs to check the work and own the result.
A manager gives a copilot a meeting transcript and account record. It drafts the client update, then the manager checks the claims, changes the judgment calls, and decides whether to send it.
A large language model is an AI model trained on broad language data to predict and generate text.
An LLM can read, classify, summarize, and draft, but your firm still needs to supply current records, limit access, check completion, and name someone to handle exceptions.
An LLM drafts an intake summary from an approved client-call transcript. The workflow supplies the right scope, blocks unsupported promises, stores the summary, and sends missing information to an owner.
More about Large language model (LLM)
#An AI model is a trained component that turns an input into a prediction, label, or generated result.
A model gives you one result inside a larger system. Your firm still has to supply current data, set permissions and rules, check the work, and handle exceptions.
A model drafts a project update from approved notes. The system adds current scope, checks unsupported claims, stores the draft, and sends missing facts to the project owner.
An AI prompt is a job brief that gives a model the instructions, examples, and task-specific information for one request.
A prompt guides one result. Repeated client work also needs current records, tests, permissions, ownership, and a clear trail showing what happened next.
A renewal-proposal prompt can supply the client record, active scope, current rates, an approved example, and the fields the finished draft must contain.
Context is the information a model can use while handling one current request or workflow step.
The model needs the current facts, rules, examples, and tools for this step. A file sitting somewhere in your company won't help until the system makes the relevant information available.
A renewal workflow can supply the signed scope, current rate, approved proposal example, and unresolved account questions for this client's next proposal.
A context window is the maximum amount of information an AI model can consider in one request, measured in tokens.
A bigger window holds more information, but that doesn't mean the model will use it all well. Give it the current facts and rules it needs, then check whether it found them.
A proposal assistant may have room for a full client archive but work better with the current brief, approved pricing rule, and three relevant examples.
Context engineering gives an AI the current instructions, data, examples, tools, and history it needs to complete a task well.
If the workflow gathers the right records and rules, your team doesn't have to rebuild the same set of information every time they ask AI to do the job.
A proposal agent receives the current client record, approved rates, claims rules, one accepted example, and tools for checking missing facts.
More about Context engineering
#An AI tool is a named function with set limits that a model can request to read outside information, calculate something, or act through another system.
A tool lets AI read or change things outside the model. Your firm has to decide whose account it uses, what it can reach, what needs approval, how to check it worked, and who fixes failures.
A project-health tool reads approved task dates for one client and returns the source record IDs. A separate, more restricted tool creates an internal follow-up task after the lookup passes its checks.
An AI skill is a reusable set of instructions that teaches an AI how to perform a particular kind of work.
A tool lets AI do something, and a skill teaches the method around it, what to collect, which rules to follow, what resources to use, and how to check the result.
A proposal-writing skill can define what information to collect, which template to use, and how the final proposal should be checked.
Model Context Protocol (MCP) is an open standard that gives AI applications a common way to connect to outside data and tools.
MCP can save you from building the same connection for several AI applications. Your firm still decides who gets access, what needs approval, how results are checked, and who fixes failures.
An AI assistant reads an approved project record through one MCP capability, then asks to create a follow-up task through another.
More about Model Context Protocol (MCP)
#Retrieval-augmented generation (RAG) searches external sources for relevant information and gives that evidence to an AI before it answers.
RAG can give you current, specific answers you can trace to company sources. Your firm still has to keep those sources reliable, control access, and test what the search returns.
Before answering a staff question, the system searches current operating procedures and supplies the relevant sections to the model.
More about Retrieval-augmented generation (RAG)
#AI grounding ties a model's claims to specific evidence that a person or system can inspect and verify.
Grounding lets you see where an answer came from, so you can check whether the record is current, supports the claim, and is something this user may see.
A client asks whether extra work is included. The system finds the signed scope and approved changes, then cites the clause that answers the question.
An AI hallucination is a false or unsupported claim that an AI presents as true.
A false claim can sound just as convincing as a true one, so client work needs evidence checks and a way to correct what gets through.
A client report says retention rose three points, but nothing in the approved data supports that number.
AI memory is a record that a system saves outside the current request and retrieves for later work.
Memory lets later work use selected facts and pick up unfinished tasks. You still need to know the source, who owns it, which client it belongs to, and how to correct or delete it.
A proposal agent saves a client's approved reporting preference and retrieves it for the next proposal, without bringing in another account's preferences.
Multimodal AI works with more than one kind of information, such as text, images, audio, video, or documents, and connects what it finds in them.
Service work arrives as photos, calls, videos, documents, and text. Multimodal AI can use them together, so someone doesn't have to turn everything into text before the work can move.
A multimodal system reads an inspection report, examines the site photos, checks the prior work order, and uses all three to draft a recommendation.
A reasoning model is a language model trained to do extra work planning, checking, and revising before it answers a difficult question.
Reasoning models can help with difficult planning and review, but they cost more and take longer. Use them where your tests show enough improvement to justify both.
A fast model extracts fields from a client report. A reasoning model handles an exception where the report conflicts with the signed scope and three downstream commitments.
An AI token is a unit of text or other information that a model reads or generates, such as a word, part of a word, punctuation mark, or image patch.
Tokens count toward context limits and the model bill. Before changing a workflow to use fewer, check what work passed, how many retries it took, and how much review it needed.
A status-report workflow logs the tokens used to read the current scope and draft the report, then records whether the result passed review and how many minutes the reviewer spent.
Fine-tuning is extra training that adapts an existing AI model to a particular task or output.
Fine-tuning can help a model handle repeated work more consistently. I would consider it after better instructions, current information, retrieval, and a written test still show the same failure.
A firm trains a model further on approved project briefs, so new briefs put the risks, scope, and named owners in the right sections.
An eval is a repeatable test that scores AI output against a written standard, so you can track quality over time.
An eval helps you check whether AI work meets the standard your clients need, instead of waiting for a complaint to tell you something went wrong.
An eval scores 20 recent proposals against the same checks, the approved price, matching scope, supported claims, a named owner, and the right client details.
A guardrail is a rule that limits an AI system's access, actions, or freedom to act without a person's approval.
Your first guardrails are usually account permissions and approval steps. They limit what a bad action can cost before the system gets a chance to take it.
An agent can draft a client reply and leave it queued, but its account cannot send email. That missing send permission is the guardrail.
Human in the loop means placing a person at a defined point in an AI process to review, approve, correct, or take over the work.
Put review where a mistake costs you and a person's judgment can help. Asking someone to approve every step can leave them overloaded without catching the important error.
An AI-drafted proposal waits for a named senior person to decide on pricing. Formatting and filing can run without that same approval step.
AI orchestration coordinates the models, agents, tools, people, and fixed steps that carry one job from the start to a record of the finished work.
It makes clear who owns the job, how work passes between steps, what needs approval, and what happens when something fails or needs to run again.
A proposal run finds the right CRM record, applies pricing, drafts the document, waits for scope approval, and files the result, with someone named to handle failures.
Computer-use AI is a way for AI to operate software through the screen, keyboard, and mouse, using screenshots to choose what to click or type next.
Computer use can help AI work with an old portal that has no useful API. But it has to interpret the screen, so you need limits, a way to check the result, and a plan for failed runs.
The system opens a client portal, finds the correct project, enters an approved status, checks the confirmation screen, and records the result.
An API is a software interface that lets one system ask another system for data or an action.
An API lets your AI system ask for a CRM record or a project update directly, instead of finding buttons on a screen. That usually makes the connection easier to test and limit.
A project workflow reads the approved client record through an API, then creates one internal follow-up task with a saved request ID.
Generalization asks whether a rule learned from observed cases still works on cases it has not seen.
A rule can handle every example you gave it and still fail on the next client. Test it on new cases, and check whether you've added limits the work doesn't need.
You write a proposal rule from ten requests, then test it on new clients, scopes, and exceptions to see whether it still works without unnecessary restrictions.
AI enablement is the work of helping a business get a useful result from AI, by preparing its information, changing the process, helping people learn, and testing small builds.
If the goal is just to be ready for AI, it's hard to know when you're done. Name the business result you're working toward so you can tell whether the work helped.
A tool that scored sales calls against a rubric produced reports no supervisor would read. Cutting it down to a short list that surfaced the low scores first is what made it useful, and that cut was the work.
Agent washing is marketing that relabels assistants, chatbots, or older automation as AI agents without giving them substantial agentic capabilities.
A product label does not tell you who starts the work, what it can reach, or whether it can choose and complete a job.
A vendor calls a chatbot an agent, but a person still opens it, pastes every customer detail, chooses the next step, and carries the answer into the CRM.