AI Lexicon

A machine-based system that makes predictions, recommendations, decisions or new content toward goals that people set.

The NIST CSRC Glossary says: "A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments."

Also called Artificial intelligence

A kind of AI that creates new content (text, images, audio, code).

A pattern where a model does some tasks well and fails at others, and the line between them is uneven and hard to predict, even for tasks that seem equally hard.

Also called Jagged edge, Jagged intelligence

The trained set of numbers (parameters) and structure that takes an input and produces an output; the part of an AI system that does the predicting.

The Google for Developers Machine Learning Glossary says: "In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions."

Also called AI model

• Foundation model A model trained on broad data, mostly with self-supervised learning, that can be adapted, for example through fine-tuning or post-training, for a variety of downstream tasks.
• Frontier model A foundation model that is among the current 'best-of-the-best' in the world; the set changes every few months.
• LLM A very large model trained on huge amounts of text to predict the next word, which lets it write, summarize, answer questions and follow instructions.
• Open weights A model whose weights (numbers that the model learned in training) are published for anyone to download and run, usually without the training data or code, and under the vendor's license terms.
• Reasoning model A model trained to work through a problem in a sequence of steps and plan its answer before it responds, spending more time and tokens to do better on hard tasks.
• Small language model A language model that is small relative to frontier models, usually a few billion parameters, and can be run cheaply, often on local hardware.

A way of building software where a model learns patterns from examples in data instead of following rules a person wrote; also the field that studies it.

The Google for Developers Machine Learning Glossary says: "A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems."

Also called ML

A property of a model that can take in or produce more than one kind of media, such as text, images, audio or video.

Not all models are able to generate images and audio.

An AI system released with its weights, its code and enough detail about its training data that anyone can use, study, change and share it for any purpose.

The Open Source Initiative says: "An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to: Use the system for any purpose and without having to ask for permission. Study how the system works and inspect its components. Modify the system for any purpose, including to change its output. Share the system for others to use with or without modifications, for any purpose." Many models sold as open source are really open weights under a restrictive license, and don't meet this test.

Also called Open source model

The act of running a trained model to produce an output from inputs, as opposed to training it.

Also called Model serving, Serving

• Batch inference A type of inference where many requests are sent together and answered later, usually at a lower price.
• Reasoning effort A setting which controls how long or how many tokens are spent by a model reasoning before providing an answer.

The limit of how many tokens a model can hold in one call, counting both what you send and what it writes back.

A numerical representation of the meaning of text, words or phrases (or an image), so that items with similar meaning sit close together and can be found by meaning, not keyword.

Also called Vectors

• Embedding model A model that creates embeddings from content it is given, usually to fill a vector database and later to turn each search question into an embedding.

The latest date used in the data that trains a model.

The numeric values a model learns in training, which decide how it turns an input into an output; their count, such as 8 billion, is the usual measure of a model's size.

A unit of text that a model reads and writes in, a whole word or a piece of one, on average about three-quarters of an English word.

The process of supplying data (often in large quantities) to a model so that it can identify patterns and make predictions.

Also called Model training

• Fine-tuning A further round of training that adapts a pretrained model to specific tasks or a particular domain.
• Post-training The later stage of training that shapes how a model acts and behaves to match its creators' intentions, and builds skills such as following instructions, reasoning, and coding.
• Pretraining The first and largest stage of the training process that involves supplying broad or general data for a model to learn from.

A property of a system or check where the same input(s) always gives the same output(s).

Also called Rule-based

A property of a system or check that works on likelihoods, so the same input(s) can give different outputs, and any single answer can be wrong some of the time.

Also called Non-deterministic, Stochastic

A way of working in which an agent plans and carries out the work, decides its own next steps, and uses tools to complete a task or goal.

Also called Process AI, workflows, outcome-driven AI, Agentic AI

• Agentic workflow A workflow with fixed steps, set by people or code, where a model handles some of the steps and uses judgment inside them.
• Agentic coding A type of programming where a person gives a goal or loose instructions, and an agent decides its own next steps to plan, write, run and test the code.
• Software factory A software development pipeline where people supply the instructions and checks, and agents do most of the building, testing and fixing.
• Vibe coding A way of building software where a person describes what they want and accepts the code AI tools write without reading it, judging only whether the result works.

A way of working where a person decides each step and does the task, and AI tools help with parts of it.

Also called AI-augmented

• AI-assisted coding A type of programming where a person decides each step and describes what code to write, and AI tools suggest and complete code based on that instruction.
• AI-assisted workflow A workflow where a person decides each step and carries it out, and AI tools help with those steps.

Content whose substance an AI tool produced, even if a person prompted, edited or approved it.

Also called Machine-generated, Synthetic content

A general-purpose AI app that you can talk to, such as the Claude app or ChatGPT.

Also called AI assistant, AI chat appAvoid Chatbot

• Custom assistant An assistant set up with fixed instructions, documents and sometimes tools for one role or team.

A rule-based workflow that runs a process from pre-set rules or pre-written code.

Can include AI as a step. If a model handles some steps and uses judgment inside them, it becomes an agentic workflow. If an agent decides its own next steps, it is agentic.

Also called RPA, Rules-based automation, Process automation

An AI system that works toward a goal by deciding its own next steps and using tools to take actions.

What makes something an agent is that the model, not fixed code, decides what to do next. Describe any agent with four questions: what you hand it (autonomy), whose permissions it uses (identity), what it can change (permissions), and what starts it (trigger).

Also called AI agent

• Coding agent An agent whose main job is writing and changing software.
• Personal agent An agent that acts on your behalf, using your permissions.
• Background agent An agent that runs in the background, often in the cloud, while you do other work, and reports back when it is done.

An agent with its own identity and permissions in your company's systems.

It is set up and tracked like an account, not borrowed from a person, and it can run with nobody present.

Also called Digital worker, AI employee, Agentic workforce, Digital workforce, Digital labor

• Internal agent One agent that many people in a company can give work to.

A setup where multiple agents split up one job and coordinate, working simultaneously or in sequence.

Also called Agent swarms, Multi-agent orchestration, Orchestrator-workers, Subagents

• Orchestration The coordination and triggering of agents or steps: deciding what runs when, passing results between them, and handling failures.
• Subagent An agent that another agent starts to do one delegated task in its own independent session, returning the result when done.
• Router agent An agent which has responsibility to sort incoming requests and pass them to the appropriate agent.

The account an agent acts under, which sets who it appears as and whose permissions it uses.

There are three patterns. It uses your login and appears as you. It acts for you under its own name, with some of your permissions. Or it has its own identity and permissions, granted by an admin. The pattern decides what the logs show, what it can access, and how IT can switch it off. It is a separate question from how autonomous the agent is.

Also called Non-human identity (NHI), Machine identity

• Acts as itself An agent using its own identity and its own permission set to take actions.
• Acts as you An agent using your identity and permissions to take actions and appearing as you.
• Acts for you An agent that acts for you under its own identity, with a limited share of your permissions, so logs usually show both the agent and you.

A scale of how much you hand over to an AI system or agent: nothing to act on, an instruction, an outcome, or an area to look after.

Autonomy is a setting you choose, not a feature of the model. The same product can run at different levels. As autonomy rises, decide how a person oversees it (see Human oversight).

Also called Autonomy ladder, Levels of agency

• Autonomous The top level of autonomy, where you hand an agent an area to look after and it acts on its own without a person approving each action.
• Advisor A level of autonomy where an AI system or agent only gives advice, and does not take actions or make decisions.
• Task runner A level of autonomy where you give an AI system or agent one specific instruction, and it acts only on that task.

Human involvement in an AI process to monitor, govern or stop it, before it acts, after it acts, or only for certain actions.

Also called Human review, Human supervision

• Human-in-the-loop A setup where a person approves each action before it happens.
• Human-on-the-loop A setup where an agent acts on its own while a person watches and can step in or stop it.
• Human-out-of-the-loop A setup where an AI system or agent runs with no person watching, reviewing or approving its work.

A control that stops an agent or model at once.

A rule that an AI system or agent gets only the permissions it strictly needs for the task, and nothing more.

Also called Least privilege, Scoped access

Everything the model can see when it answers: your instruction, the conversation so far, and any documents or tool results added to the call.

Text or data given to an AI system, usually by a user and often as an instruction, that is only part of the context the system sees when it answers.

Also called Instruction

• Few-shot prompting A method of prompting where you supply some examples of inputs and the answers you expect from the model.
• Prompt chaining A simple workflow that uses the output of one prompt as the input of the next.
• Prompt template A shared prompt with sections that a person or a program fills in before sending.
• Role prompting A method of prompting which instructs the model to act as a certain human-like role or persona.

The practice of choosing and managing everything a model sees on each call, not just the wording of the prompt.

Wikipedia (Prompt engineering, 2026) says: "Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens."

Also called Context engineer

A note that an AI system saves from past sessions (facts, preferences, decisions) and brings back into later ones to help guide it, since the model itself remembers nothing between calls.

Also called Long-term memory, Persistent memory

A continuous stretch of work, such as one chat or one run, in which the AI system carries earlier messages and work forward until it ends.

Also called Chat, Conversation, Thread, Run

The practice of crafting an instruction so that the model executes it well.

Also called Prompt design, Prompting

The structure around a model that turns it into one working agent: its interface, agent loop, own context and memory, permissions, and list of allowed tools.

The test for what belongs in it: does each agent carry its own, or do they all share one? If each agent has its own, it's harness. If they share it, it's stack. A harness wraps an agent, not a person. One person can run several agents, each with its own harness, and an agent can run with no person attached at all. Your personal assistant is one agent with one harness; that it's yours is about who it acts for, not about the harness belonging to a person. Why the agent is the unit: if the line were drawn around a person, a coding agent and a research agent run by the same person would be lumped into one harness, even though their loops, tools and permissions are completely different. The test stops working. Components that sit on the line: - Tools: which tools an agent may use is harness; the shared connector behind each tool is stack. A tool local to one person's setup is harness; a centrally hosted one is stack. - Permissions: the policy for one agent is harness; the central identity system that enforces it is stack. - Gateway: stack, because it is shared. People disagree on this one, so say it explicitly. Harness vs stack: the stack is the shared foundation: gateway, inference, data and ontology, evals, central identity, and shared connectors. The same harness concept appears in both the build stack (e.g. Claude Code: terminal, files, shell, git, human in the loop) and the product runtime stack (e.g. a support bot: narrow interface, business systems, tight permissions, little oversight). Same components, different tuning. Promotion is how something moves from a harness into the stack. The word grew up around personal coding agents (Claude Code, Codex, OpenCode), but it applies to customer-facing AI too: a product owner picks a harness for a chatbot. Choosing Claude Code vs OpenCode is mostly a harness decision, not an interface or model decision, since they can call the same models.

Also called Scaffold, Scaffolding, Agent runtime, Harness, Agent infrastructureAvoid Harness engineering; Agent framework

• System prompt A prompt written by the developer or vendor, not the user, that the app sends alongside every user-supplied prompt to set the model's role, rules, and tone.
• Agent skill A package of instructions and files, written in an open text-based format, that an agent loads only when a task needs it.
• Agent loop The cycle an agent repeats: decide the next step, take it with a tool, look at the result, and go again until the job is done or something stops it.
• Client The software that opens the connection and makes the call, to a model or to an MCP server.
• Interface The way a person interacts with an AI system: free text, document and image upload, audio, buttons and forms, in a chat window, terminal or IDE.

The practice of tying an AI system's answers to a real source that can be verified and checked.

Also called Grounded generation, Source grounding

An intermediary that your company's calls to models route through; when it holds the only API keys, it is both a control point and a log of every call.

Keep the two benefits separate: control (key custody, nothing routes around it) and observability (logs of what goes into and comes out of each call). Most gateways also give one API across model providers, with failover and cost tracking. Routing is a capability of the gateway: deciding in real time which model a request goes to. The decision can be a deterministic rule (size, cost, who is asking) or a model that classifies the request. It usually trades off cost, latency and capability: send easy work to a cheap model, escalate hard work to a frontier model. The catch is that the router has to guess how hard a request is before the work is done. Router and gateway can be separate, but splitting them is extra work most companies shouldn't take on.

Also called LLM gateway, AI gateway, model gateway, LLM router, model routing

• Model routing A step that picks, for each request, which model handles it, using a fixed rule or a model that judges the request.

A technique for grounding that finds relevant text in your documents or data and adds it to the model's context, so the model writes its answer from that text without being changed.

Also called Retrieval-augmented generation

• Vector database A type of database that stores embeddings and can find the closest match to a query by meaning.

The shared AI infrastructure that every agent and harness in your company draws on, such as gateways, model access, data, evals, central identity and shared connectors.

Also called AI stack, Tech stack

A packaged link to one outside system, such as Google Drive or Salesforce, that gives an agent a set of tools to work with it.

Also called Integrations, Apps, Plugins

A curated collection of documents that a person or an AI system can access and use as context.

Also called KB, Knowledge store

A single action, such as running a task or a function, that an agent can call.

Where a tool lives depends on where it can be reached, not who built it. A centrally hosted tool that others can use is part of the stack. A local tool scoped to one person's harness, like an advanced developer's local MCP server, is part of that harness. An agent's permission to use a tool is always harness. Building a central tool is a stack contribution; wiring your agent to it is harness configuration. Today tools are mostly central and shared; local, harness-only tools are the exception. Watch: personal agents may start creating tools on the fly for one person or task, which would shift part of the tool layer into the harness.

Also called Tool use, Tool calling, Function calling

• Computer use A tool that lets an agent work a computer screen the same way a person would (view, click, type).
• Code execution A tool that runs code the model has written, usually in a sandbox.

An open standard, created by Google and now run by the Linux Foundation, that lets agents from different vendors communicate and work together.

The A2A Protocol project (Linux Foundation) says: “The Agent2Agent (A2A) Protocol is an open standard for seamless communication and collaboration between AI agents.”

Also called Agent2Agent, Agent-to-agent protocol

A control point in front of everything an agent can act on (tools, APIs and MCP servers) that governs what it can reach, enforces least privilege and logs its actions.

Used three ways today: the outbound control point for agent actions; an MCP gateway in front of your tool servers; and a registry where agents get their own identities and permissions. The control point and the registry are two views of one job: where rules are enforced vs where they are stored. Once an agent can act, the risk is a real side effect (a write, a payment, a deletion), not just a bad answer.

Also called Tool gateway, MCP gateway, Tool-access gateway

A discipline concerned with the AI lifecycle and running AI in production.

Also called LLMOps, MLOpsAvoid AIOps

• Observability The tracking and recording of what an AI system does on each run: inputs, outputs, steps, tool calls, cost and errors.

A practice of building where an agent keeps rerunning and checking its own work, with set rules for when it stops and when it asks a person to step in.

An open standard, started by Anthropic in 2024 and now run under the Linux Foundation, for connecting AI systems and agents to outside tools and data in one common way.

Wikipedia says: “The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources.”

Also called MCP

• MCP server A program that uses MCP to offer tools or data to agents, running on your own computer or as a hosted service; it is one way to build a connector.

An isolated environment where an agent can execute code or take actions without accessing live systems or data.

Also called Agent sandbox, Isolated environment

A measurement of how well an AI system does something (tone, helpfulness, accuracy, safety), usually as a score, graded by code, a model or a person.

An eval doesn't have to be structured or a comparison: a one-off tone check on a single customer-service transcript is an eval. Not limited to models; you can eval a harness, a context setup, or other AI infrastructure. Comparison and benchmarking are uses of evals, not the definition. Edge case: an eval can have a deterministic answer (did the model solve the math problem?) and still be an eval, because it measures capability.

Also called Evaluations, Model evaluation, AI testingAvoid Vibe check, Vibes-based eval

• Benchmark A fixed, standardized eval suite held constant so comparisons are fair: across models, across approaches, or over time.
• LLM-as-judge An eval where a model grades the work of another model or system.
• Regression eval An eval designed to protect a working AI feature against unintended change.
• Verified test set A set of test cases that have had correct answers verified by a subject-matter expert.
• A/B test An experiment where live users are split between two versions of a product or service and the results are compared.
• Human-as-judge An eval where a person reviews a system's output and judges its quality.
• Production monitoring The practice of running evals on a live system to score the quality of its outputs.
• Side-by-side eval An eval that compares the outputs of two or more models or systems next to each other to judge which is better.

Testing which imitates an adversarial attack on an AI system in order to learn more about the safety and security of the system.

Red teaming is not a new concept, but it has some unique applications for AI systems. They can be attacked through plain language (prompt injection, jailbreaks), and they can produce harmful output with no attacker at all, so red teams test for both.

Also called AI red teaming, Adversarial testing

The practice of protecting against harm that an AI system may cause or help cause, whether by its own mistakes or by doing what a person asks when it shouldn't.

AI systems can sometimes act in a way that is harmful (e.g. deleting the wrong files, giving bad advice) even without an external party that has malicious intent. Systems need to be protected against themselves by having guardrails and safety nets to prevent unintended outcomes.

A check, built into the harness or written into the instructions, that screens what goes in, what comes out, or what an agent tries to do.

Guardrails are an essential component of a robust AI system that has AI safety and AI security protections. Checks that run in code outside the model are much stronger than rules written in the instructions, which a model can ignore and an attacker can override. They can also be an important part of a product experience, ensuring that the system is behaving as intended and steering it back on track if not.

Also called Safety filters, Content filters, Policy checks, Input/output validation

• Deterministic guardrail A guardrail that uses fixed rules or code so that it applies the same way every time.
• Input guardrail A guardrail that checks or filters what goes into a model before the model processes it.
• Output guardrail A guardrail that checks or filters what comes out of a model before anyone sees it or it takes effect.
• PII redaction A type of guardrail that removes or masks personal data in text passing through a system, usually before it reaches a model or log.
• Probabilistic guardrail A guardrail that uses a model or classifier, so its judgment can be wrong: it can miss bad content, block good content, or be tricked.

Output from an AI system that is false or made up but stated with confidence, such as wrong facts, faulty reasoning or sources that don't exist.

Hallucinations are what make an AI system especially risky. Even if a hallucination is rare, it can jeopardize the safety of an entire system. Hallucinations are products of the probabilistic nature of models. Grounding in real sources, asking for citations and checking the output reduce them, and guardrails can catch some, but nothing removes them. Newer models hallucinate less on some tasks, but not on all of them.

Also called Confabulation, Fabrication, Making things up

A model's tendency to tell you what you seem to want to hear.

When prompting a model, if you ask it to agree with you, it is likely to do so, or at least be agreeable. Models also flatter and agree when you don't ask, and often back down from a correct answer when you push back. This is dangerous for people who tend to prompt in a way that asks for their preconceived notion to be validated rather than questioned. Sycophancy is generally considered a negative attribute of models.

Also called People-pleasing, Flattery, Glazing

Low-value, likely flawed AI output released without adequate checking by a person or a system.

AI slop is defined by a result, not a tool. It's usually obvious due to a pattern that seems wordy, unnatural, or recognizable, and can be repulsive to people. It can be damaging to credibility and confidence in a person or company.

Also called Slop, Workslop, Slop grenade, Slopper, Deckslop

The degree to which a model acts in line with human values and the goals and intent of the people directing it.

There are two levels to alignment: Broad: does the model act in line with human values and the intent of an org? Everyday: does this system actually do what you want? Labs train models for alignment, but the useful question is always: aligned to whom?

The practice of defending an AI system, and the data and tools it can reach, from attackers and deliberate misuse.

AI security is concerned with defending a system from misuse, especially intentional misuse that could jeopardize the model, its data, its tools or its outputs. AI safety covers harm with no attacker, including accidents.

Also called Security for AI, LLM security

• Data exfiltration A type of AI system attack which attempts to have the system send private data outside your company or to anyone not permitted to see it.
• Indirect prompt injection A type of prompt injection where instructions are hidden in content that the AI system reads (web page, email, file, etc.).
• Jailbreak A type of AI system attack which attempts to 'convince' a system to ignore its rules, guardrails, or system instructions.
• Prompt injection A type of AI system attack where text the system reads contains instructions, and the system follows them as if they came from its user or owner.

A skew in how an AI system treats people or cases, or in what it produces, inherited from its data or built in by design.

Bias is inherently present in an AI system. Data cannot be unbiased, so systems that use it, especially systems that train on it, cannot be unbiased. Not every skew is a bad thing. A deliberate skew, such as favoring recent documents, can improve a system. The harm is bias that treats groups of people unfairly. The challenge for areas like AI ethics is to find that bias, measure it, and decide what is fair to people.

Also called Algorithmic bias, AI bias

Image, audio or video made or changed with AI so that it falsely appears to show a real person, place or event.

The EU AI Act (Regulation (EU) 2024/1689, Art. 3(60), 2024-06) says: "‘deep fake’ means AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events and would falsely appear to a person to be authentic or truthful;"

A failure where a model or agent games how it is measured, getting a high score without the intended outcome, such as editing tests so they pass instead of fixing the code.

A set of AI tools that your company has vetted and approved for work use, often only for certain data or tasks.

Also called Sanctioned AI tools, Allowlist

• Shadow AI The use of unapproved AI tools, or personal accounts on approved ones, for work tasks with work data.

The ways an AI system can use your company's data: reading it during a task, which leaves the model unchanged, or training on it, which changes the model.

Also called Training on our data

• Fine-tuning on our data Adjusting the weights of a model by training it further on your own data.
• In-context use Supplying company-specific data to a model in the prompt so that it can provide a more relevant response, without changing the model.
• Retrieval from our data RAG applied to your company's data: the AI system fetches the relevant data as needed to inform a response or action.
• Vendor training A vendor using your company's data, such as prompts and outputs, to train or improve its AI models.

The practice of AI ethics: turning principles like fairness, safety, privacy and accountability into actual controls.

Also called Trustworthy AI, Ethical AI, RAI

• AI ethics A field of inquiry into what is right and fair in how AI is built and used: the principles and the 'what should we do' questions.

A structure of accountability, oversight, and policy for how your company builds, buys, and uses AI.

Also called AI risk management, AI compliance

A standing group of leaders from legal, risk, security, IT and the business that sets your company's AI policy and approves higher-risk AI uses.

Also called AI council, AI steering committee, AI governance board

A list of the AI systems that your company uses or builds, each with an owner, purpose, and risk level.

Also called AI register, Model inventory, AI system inventory

A governance problem where agents multiply without control, so no one knows which agents exist, who owns them, what they are for, or what they can access.

A company or other body that uses an AI system under its own authority, as opposed to the provider that makes it.

The EU AI Act (Article 3(4)) says: "a natural or legal person, public authority, agency or other body using an AI system under its authority except where the AI system is used in the course of a personal non-professional activity." Many people assume a deployer is the builder. Under the Act, a company that uses an AI system at work is a deployer; the company that makes it is the provider.

Also called AI deployer

The ability to understand how or why a model reached a conclusion and produced a given output.

Also called Explainable AI (XAI), Interpretability

An AI system used where errors can seriously affect people's health, safety or rights, such as hiring, credit, education or critical infrastructure, which brings strict legal duties.

The EU AI Act sets out which systems count in Article 6 and Annex III. Providers and deployers of these systems have extra duties, such as risk management, human oversight and record keeping. Some US state laws, such as Colorado's, use their own definitions, and these have changed recently, so check the current text.

Also called High-risk AI system

An international standard, published in 2023, that sets requirements for the policies, roles, risk checks and reviews your company uses to manage AI, and that an outside auditor can certify against.

Also called ISO 42001, AI management system (AIMS)

A risk discipline, set by bank regulators, that inventories, independently validates and monitors the models a bank relies on, so that a wrong or misused model does not cause losses.

Also called MRM, AI risk officer

A company or other body that builds an AI system or model, or has one built, and offers it under its own name, paid or free.

The EU AI Act (Article 3(3)) says: "a natural or legal person, public authority, agency or other body that develops an AI system or a general-purpose AI model or that has an AI system or a general-purpose AI model developed and places it on the market or puts the AI system into service under its own name or trademark, whether for payment or free of charge." A company that only uses a system is a deployer. It becomes the provider of a high-risk system if it puts its own name on it or makes a substantial change to it (Article 25).

Also called Developer, AI provider, Model provider

A person driving AI adoption and enablement inside a team or function.

The local person who helps colleagues actually use the tools, surfaces use cases, and connects the floor to the central AI effort. Embedded and usually part-time and unofficial, unlike a central, dedicated center of excellence (CoE).

Also called AI ambassador, AI advocate

A senior executive who is accountable for your company's AI strategy, adoption and governance.

Also called CAIO, Head of AI, AI leader

A person or team whose job is to measure AI systems using verified test sets, benchmarks and regression evals.

Also called Eval engineer, AI evaluation engineer, AI quality engineer

A person whose job is to oversee a group of agents at work, by setting their tasks and standards, checking the quality of their output, and fixing them when they fail.

Also called Agent supervisor, Agent operator

An enterprise org that is dedicated to improving the usability of AI within the company.

Also called AI CoE, Center of excellence, Hub-and-spoke model, AI enablement team, AI platform team

A person responsible for AI features built into products.

Also called AI PM

An employee who sits outside the formal IT team and uses tools available to build software to support their team or company.

Also called Agent builder, Maker, Business technologist

An engineer who works inside a business unit to build a working solution for the company through to production.

An FDE often identifies the goals and steps that make up a job, which is the groundwork Agentic needs.

Also called FDE, Deployment engineer, Embedded engineer, Deployment strategist, Applied AI engineer

• External FDE An engineer from a software or AI vendor who works inside a customer's company to build a working solution on the vendor's product and get it into production.
• Internal FDE An engineer on your company's own staff who sits inside a business unit to build and run AI systems for that team's work.

A person who uses AI tools to take a product from idea to launch alone, doing the work of a product manager, a designer and an engineer.

LinkedIn made this a formal title in late 2025 and opened it to people from any function. Elsewhere, the word builder is mostly a label product managers use when they build working software with coding agents instead of handing specs to engineers. It overlaps with product engineer, which reaches the same scope from the engineering side. It matters because it signals that some companies now plan teams around one person per product idea instead of three specialists.

Also called Associate product builder

A role which entails responsibility to design and build features through production to meet a user need.

A named person, usually a business leader, who is accountable for one use of AI in your company, including its results, its risks and its compliance.

Also called AI system owner, Business owner

Mental fatigue from guiding, steering and overseeing more AI tools and output than you can handle.

The ongoing work of understanding how AI works, where it fails, and how to use it well.

Also called AI fluency, AI upskilling

A person, team, or company that is fully on board with working AI-first.

Also called AGI-pilled

The growing gap between what you have produced with AI and what you understand, which you pay for later when you need to fix, explain or build on it.

A drop in hiring for early-career roles in jobs exposed to AI, as AI takes over the routine work that junior people used to learn on.

Also called Missing rung, Canaries in the coal mineAvoid Broken rung (McKinsey and LeanIn use it for women passed over for their first promotion to manager)

A person who approves or passes along AI output without verifying or reading it.

A wave of restructuring that cuts layers of middle management and gives each remaining manager more people to lead, often with AI named as the reason.

Also called Flattening, Delayering, Unbossing

The practice of measuring AI adoption by the number of tokens used, which pushes people to run up usage to look productive.

The posted price of input and output tokens consumed or generated.

Also called Token price, Price per million tokens

A claim that overstates how much AI a product, service or company uses, or how capable it is, to attract customers or investors.

Usually marketing.

Also called Agent washing

The total cost of completing one task with an AI system, counting every model call, retry and tool call, and ideally the human review time.

Also called Tokenomics, Cost per outcome, Cost per resolution

A design that lets your company, product or service swap one model or vendor for another with little rework, so it is not locked in to one provider.

Also called Multi-model, LLM-agnostic, Vendor-neutral

An approach where a country or company keeps its AI (models, data and computing) under its own control and laws, insulated from foreign vendors and jurisdictions.

Also called AI sovereignty, Sovereign cloud

A pricing model based on consumption, such as per token, per call or per task, so cost rises and falls with use.

Also called Consumption pricing, Pay-as-you-go, Metered pricing, API pricing

A product that is mostly valuable because of basic and simple features that add onto an existing model.

Also called Thin wrapper, GPT wrapper, LLM wrapper

A marketing practice to shape content so that AI search and answer tools are more likely to find it and cite it.

A lasting advantage, hard for competitors to copy, that makes a company, product, or service less likely to lose business.

Also called Defensibility, Competitive advantage, AI moat

The first real, usable version of a product: the smallest scope that delivers value to real users, with planned near-term improvements.

The original startup meaning is the smallest version that tests whether people want the product at all. In large companies it has come to mean the first production release.

Also called Minimum viable product

A pricing model which is linked to the result of using a product or service.

Also called Outcome pricing, Pay per resolution, Results-based pricing

A pricing model with a fixed fee for each person per month or year, no matter how much they use it.

Also called Per-user pricing, Seat-based pricing, Per-user licence

A release of a product to a limited group of real users doing real work, for a limited time, with a planned short-term review of its viability or effectiveness.

Also called Pilot program, Trial, Limited rollout

The state of a process or product that real work depends on and expects to be reliable.

Also called Prod, Live, General availability (GA)

A small test of whether an idea or approach can work at all, usually on sample data and without real users.

Also called POC, PoC