Adversarial Attacks
Adversarial attacks are deliberate attempts to make an AI system behave incorrectly or bypass its safeguards.
Read the guideThe language of AI at work, clearly explained.
Practical definitions for founders, operators, and enterprise teams building, buying, or governing AI. Written for decisions, not demos.
- Terms
- 54
- Themes
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- Reviewed
- July 2026
Find the language behind AI at work.
Every guide includes a clear definition, workplace example, practical checks, related concepts, FAQs, and sources.
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54 definitions
A
Agent Compute Units (ACUs)
Agent Compute Units, or ACUs, are a way to measure the computational work an AI agent performs.
Read the guideAgent Economy
The agent economy describes a shift toward software agents performing, coordinating, and purchasing work on behalf of people and organizations.
Read the guideAgent Harness
An agent harness is the surrounding system that gives an AI agent its tools, instructions, permissions, and evaluation loop.
Read the guideAgent memory
Agent memory is the information an AI agent can retain or retrieve to make later work more consistent and context-aware.
Read the guideAgent Swarm
An agent swarm is a coordinated group of specialized AI agents working on parts of the same objective.
Read the guideAgentic workflow
An agentic workflow is a process in which an AI agent plans and carries out multiple steps toward a goal, adapting as it encounters new information.
Read the guideAI Agents
An AI agent is software that can reason through a goal, use tools, and take steps toward completing work.
Read the guideAI coworker
An AI coworker is an AI system designed to contribute to a team’s ongoing work across the tools and context where that work happens.
Read the guideAI employee
An AI employee is an AI agent given an ongoing role, shared work context, and permission to complete defined tasks across a team’s tools. It is software, not a legal employee.
Read the guideAI Guardrails
AI guardrails are rules and controls that keep an AI system within approved data, behavior, and decision boundaries.
Read the guideAI Process Mapping
AI process mapping is the practice of documenting a workflow so a team can identify where AI can assist, automate, or require review.
Read the guideAPI Integration Test
An API integration test verifies that a system can reliably exchange data or actions with another service through its interface.
Read the guideAudit Trail
An audit trail is a chronological record of an AI system’s inputs, actions, outputs, approvals, and changes.
Read the guideAutonomous Agents
Autonomous agents are AI systems that can make and carry out routine decisions within defined goals and boundaries.
Read the guideB
C
Company Brain
A company brain is a connected, governed layer of organizational knowledge that people and AI can use to understand how work gets done.
Read the guideComplexity Threshold
A complexity threshold is the point where manual work or fixed-rule automation can no longer handle a process’s exceptions and variability well.
Read the guideContext window
A context window is the amount of information a language model can consider at one time when producing a response.
Read the guideD
E
F
Feedback Loop
A feedback loop uses outcomes and corrections from prior work to improve a process over time.
Read the guideFine Tuning
Fine tuning adapts a pretrained model to a narrower task or style using additional examples.
Read the guideFree and Open Source Software (FOSS)
Free and open source software, or FOSS, is software whose source code can be inspected, used, modified, and shared under its license.
Read the guideG
H
Hallucination
A hallucination is information generated by an AI system that sounds credible but is incorrect, unsupported, or invented.
Read the guideHuman in the Loop (HITL)
Human-in-the-loop is a workflow design in which people review, guide, approve, or correct AI work at meaningful points.
Read the guideI
K
L
M
Machine Learning
Machine learning is a way of building software that learns patterns from data rather than following only hand-written rules.
Read the guideModel Drift
Model drift is a decline in an AI system’s usefulness when the real-world data, task, or behavior it encounters changes over time.
Read the guideMulti Agent Systems
Multi agent systems use multiple AI agents with distinct roles that coordinate toward a shared outcome.
Read the guideN
O
P
PEV Loop
A PEV loop is a repeating agent pattern of planning a task, executing actions, and verifying the result before continuing.
Read the guidePrompt
A prompt is the instruction, question, context, or input given to an AI model to guide what it produces.
Read the guidePrompt Chaining
Prompt chaining breaks a complex request into a sequence of smaller AI prompts where each step informs the next.
Read the guidePrompt Engineering
Prompt engineering is the practice of designing instructions, context, and examples that guide an AI system toward a useful result.
Read the guideR
Reflection Agent
A reflection agent reviews its own intermediate result against a goal or criteria before deciding what to do next.
Read the guideRetrieval Augmented Generation (RAG)
Retrieval-augmented generation, or RAG, is a method where an AI system retrieves relevant information before generating a response or taking an action.
Read the guideRobotic Process Automation (RPA)
Robotic process automation, or RPA, automates repeatable software actions using defined rules and steps.
Read the guideS
T
Task Decomposition
Task decomposition is the act of breaking a complex objective into smaller steps that can be planned, completed, and checked.
Read the guideTokenization
Tokenization is the way an AI language model splits text into smaller units it can process.
Read the guideTool calling
Tool calling is a capability that lets an AI system request a defined action from another piece of software, such as searching, creating a record, or sending a message.
Read the guideTraceability
Traceability is the ability to connect an AI result or action back to its source data, instructions, tools, and approvals.
Read the guideTraining Data
Training data is the collection of examples used to teach a machine-learning model patterns and relationships.
Read the guideTransformer Models
Transformer models are neural-network architectures that are especially effective at processing sequences such as language.
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