Evaluation & Safety
Adversarial Attacks
Adversarial attacks are deliberate attempts to make an AI system behave incorrectly or bypass its safeguards.
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The 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
- 53
- Themes
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- Reviewed
- July 2026
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Find the language behind AI at work.
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53 definitions
A
AI at Work
Agent Compute Units (ACUs)
Agent Compute Units, or ACUs, are a way to measure the computational work an AI agent performs.
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Agent Economy
The agent economy describes a shift toward software agents performing, coordinating, and purchasing work on behalf of people and organizations.
Read the guideAgents & Automation
Agent Harness
An agent harness is the surrounding system that gives an AI agent its tools, instructions, permissions, and evaluation loop.
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Agent memory
Agent memory is the information an AI agent can retain or retrieve to make later work more consistent and context-aware.
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Agent Swarm
An agent swarm is a coordinated group of specialized AI agents working on parts of the same objective.
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Agentic 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.
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AI Agents
An AI agent is software that can reason through a goal, use tools, and take steps toward completing work.
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AI 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.
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AI Guardrails
AI guardrails are rules and controls that keep an AI system within approved data, behavior, and decision boundaries.
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AI 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.
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API Integration Test
An API integration test verifies that a system can reliably exchange data or actions with another service through its interface.
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Audit Trail
An audit trail is a chronological record of an AI system’s inputs, actions, outputs, approvals, and changes.
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Autonomous Agents
Autonomous agents are AI systems that can make and carry out routine decisions within defined goals and boundaries.
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C
AI at Work
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.
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Complexity 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.
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Context window
A context window is the amount of information a language model can consider at one time when producing a response.
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Data & Retrieval
Data connector
A data connector is a secure link that lets an AI system retrieve information from or take permitted actions in another tool.
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Deep Learning
Deep learning is a form of machine learning that uses multi-layer neural networks to learn patterns from data.
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Evaluation & Safety
Evals
Evals are structured tests that measure whether an AI system performs a task accurately, safely, and consistently.
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Explainability (XAI)
Explainability, or XAI, is the ability to understand and communicate why an AI system produced an output or recommendation.
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AI at Work
Feedback Loop
A feedback loop uses outcomes and corrections from prior work to improve a process over time.
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Fine Tuning
Fine tuning adapts a pretrained model to a narrower task or style using additional examples.
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Free 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.
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H
Evaluation & Safety
Hallucination
A hallucination is information generated by an AI system that sounds credible but is incorrect, unsupported, or invented.
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Human 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.
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K
L
M
Foundations
Machine Learning
Machine learning is a way of building software that learns patterns from data rather than following only hand-written rules.
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Model 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.
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Multi Agent Systems
Multi agent systems use multiple AI agents with distinct roles that coordinate toward a shared outcome.
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Foundations
Natural Language Processing (NLP)
Natural language processing, or NLP, is the field of building systems that work with human language.
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Neural Network
A neural network is a machine-learning model made of connected layers that learn patterns from examples.
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P
Agents & Automation
PEV Loop
A PEV loop is a repeating agent pattern of planning a task, executing actions, and verifying the result before continuing.
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Prompt
A prompt is the instruction, question, context, or input given to an AI model to guide what it produces.
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Prompt Chaining
Prompt chaining breaks a complex request into a sequence of smaller AI prompts where each step informs the next.
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Prompt Engineering
Prompt engineering is the practice of designing instructions, context, and examples that guide an AI system toward a useful result.
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Agents & Automation
Reflection Agent
A reflection agent reviews its own intermediate result against a goal or criteria before deciding what to do next.
Read the guideData & Retrieval
Retrieval 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.
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Robotic Process Automation (RPA)
Robotic process automation, or RPA, automates repeatable software actions using defined rules and steps.
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AI at Work
Self Learning Loop
A self learning loop is a process that uses feedback and outcomes to improve future system behavior over time.
Read the guideData & Retrieval
Synthetic Data
Synthetic data is artificially generated data designed to resemble real-world data for testing, training, or analysis.
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Agents & Automation
Task Decomposition
Task decomposition is the act of breaking a complex objective into smaller steps that can be planned, completed, and checked.
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Tokenization
Tokenization is the way an AI language model splits text into smaller units it can process.
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Tool 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.
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Traceability
Traceability is the ability to connect an AI result or action back to its source data, instructions, tools, and approvals.
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Training Data
Training data is the collection of examples used to teach a machine-learning model patterns and relationships.
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Transformer Models
Transformer models are neural-network architectures that are especially effective at processing sequences such as language.
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