# Luffy AI Glossary

54 plain-English definitions of AI-at-work terms.

- [AI Agents](https://www.luffy.so/ai-glossary/ai-agent): An AI agent is software that can reason through a goal, use tools, and take steps toward completing work.
- [AI coworker](https://www.luffy.so/ai-glossary/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.
- [AI employee](https://www.luffy.so/ai-glossary/ai-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.
- [Agentic workflow](https://www.luffy.so/ai-glossary/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.
- [Agent memory](https://www.luffy.so/ai-glossary/agent-memory): Agent memory is the information an AI agent can retain or retrieve to make later work more consistent and context-aware.
- [Bias Detection](https://www.luffy.so/ai-glossary/bias-detection): Bias detection is the process of finding unfair patterns in an AI system’s data, outputs, or decisions that could disadvantage people or groups.
- [Context window](https://www.luffy.so/ai-glossary/context-window): A context window is the amount of information a language model can consider at one time when producing a response.
- [Data connector](https://www.luffy.so/ai-glossary/data-connector): A data connector is a secure link that lets an AI system retrieve information from or take permitted actions in another tool.
- [Grounding](https://www.luffy.so/ai-glossary/grounding): Grounding is the practice of basing an AI response or action on specific, relevant source information.
- [Hallucination](https://www.luffy.so/ai-glossary/hallucination): A hallucination is information generated by an AI system that sounds credible but is incorrect, unsupported, or invented.
- [Human in the Loop (HITL)](https://www.luffy.so/ai-glossary/human-in-the-loop): Human-in-the-loop is a workflow design in which people review, guide, approve, or correct AI work at meaningful points.
- [Knowledge base](https://www.luffy.so/ai-glossary/knowledge-base): A knowledge base is an organized collection of documents, policies, decisions, and other information that people or AI systems can search and use.
- [Large Language Models (LLMs)](https://www.luffy.so/ai-glossary/large-language-model-llm): A large language model, or LLM, is an AI model trained to understand and generate language, including text, code, and structured outputs.
- [Prompt](https://www.luffy.so/ai-glossary/prompt): A prompt is the instruction, question, context, or input given to an AI model to guide what it produces.
- [Retrieval Augmented Generation (RAG)](https://www.luffy.so/ai-glossary/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.
- [Tool calling](https://www.luffy.so/ai-glossary/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.
- [Adversarial Attacks](https://www.luffy.so/ai-glossary/adversarial-attacks): Adversarial attacks are deliberate attempts to make an AI system behave incorrectly or bypass its safeguards.
- [Agent Compute Units (ACUs)](https://www.luffy.so/ai-glossary/agent-compute-units-acus): Agent Compute Units, or ACUs, are a way to measure the computational work an AI agent performs.
- [Agent Economy](https://www.luffy.so/ai-glossary/agent-economy): The agent economy describes a shift toward software agents performing, coordinating, and purchasing work on behalf of people and organizations.
- [Agent Harness](https://www.luffy.so/ai-glossary/agent-harness): An agent harness is the surrounding system that gives an AI agent its tools, instructions, permissions, and evaluation loop.
- [Agent Swarm](https://www.luffy.so/ai-glossary/agent-swarm): An agent swarm is a coordinated group of specialized AI agents working on parts of the same objective.
- [AI Guardrails](https://www.luffy.so/ai-glossary/ai-guardrails): AI guardrails are rules and controls that keep an AI system within approved data, behavior, and decision boundaries.
- [AI Process Mapping](https://www.luffy.so/ai-glossary/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.
- [API Integration Test](https://www.luffy.so/ai-glossary/api-integration-test): An API integration test verifies that a system can reliably exchange data or actions with another service through its interface.
- [Audit Trail](https://www.luffy.so/ai-glossary/audit-trail): An audit trail is a chronological record of an AI system’s inputs, actions, outputs, approvals, and changes.
- [Autonomous Agents](https://www.luffy.so/ai-glossary/autonomous-agents): Autonomous agents are AI systems that can make and carry out routine decisions within defined goals and boundaries.
- [Company Brain](https://www.luffy.so/ai-glossary/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.
- [Complexity Threshold](https://www.luffy.so/ai-glossary/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.
- [Deep Learning](https://www.luffy.so/ai-glossary/deep-learning): Deep learning is a form of machine learning that uses multi-layer neural networks to learn patterns from data.
- [Evals](https://www.luffy.so/ai-glossary/evals): Evals are structured tests that measure whether an AI system performs a task accurately, safely, and consistently.
- [Explainability (XAI)](https://www.luffy.so/ai-glossary/explainability-xai): Explainability, or XAI, is the ability to understand and communicate why an AI system produced an output or recommendation.
- [Feedback Loop](https://www.luffy.so/ai-glossary/feedback-loop): A feedback loop uses outcomes and corrections from prior work to improve a process over time.
- [Fine Tuning](https://www.luffy.so/ai-glossary/fine-tuning): Fine tuning adapts a pretrained model to a narrower task or style using additional examples.
- [Free and Open Source Software (FOSS)](https://www.luffy.so/ai-glossary/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.
- [Inference](https://www.luffy.so/ai-glossary/inference): Inference is the process of using a trained model to produce an output from new input.
- [Intelligent Document Processing (IDP)](https://www.luffy.so/ai-glossary/intelligent-document-processing-idp): Intelligent document processing uses AI to read, classify, extract, and validate information from business documents.
- [Machine Learning](https://www.luffy.so/ai-glossary/machine-learning): Machine learning is a way of building software that learns patterns from data rather than following only hand-written rules.
- [Model Drift](https://www.luffy.so/ai-glossary/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.
- [Multi Agent Systems](https://www.luffy.so/ai-glossary/multi-agent-systems): Multi agent systems use multiple AI agents with distinct roles that coordinate toward a shared outcome.
- [Natural Language Processing (NLP)](https://www.luffy.so/ai-glossary/natural-language-processing-nlp): Natural language processing, or NLP, is the field of building systems that work with human language.
- [Neural Network](https://www.luffy.so/ai-glossary/neural-network): A neural network is a machine-learning model made of connected layers that learn patterns from examples.
- [Observability](https://www.luffy.so/ai-glossary/observability): Observability is the ability to inspect how an AI system is behaving through its inputs, outputs, actions, errors, and performance signals.
- [PEV Loop](https://www.luffy.so/ai-glossary/pev-loop): A PEV loop is a repeating agent pattern of planning a task, executing actions, and verifying the result before continuing.
- [Prompt Chaining](https://www.luffy.so/ai-glossary/prompt-chaining): Prompt chaining breaks a complex request into a sequence of smaller AI prompts where each step informs the next.
- [Prompt Engineering](https://www.luffy.so/ai-glossary/prompt-engineering): Prompt engineering is the practice of designing instructions, context, and examples that guide an AI system toward a useful result.
- [Reflection Agent](https://www.luffy.so/ai-glossary/reflection-agent): A reflection agent reviews its own intermediate result against a goal or criteria before deciding what to do next.
- [Robotic Process Automation (RPA)](https://www.luffy.so/ai-glossary/robotic-process-automation-rpa): Robotic process automation, or RPA, automates repeatable software actions using defined rules and steps.
- [Self Learning Loop](https://www.luffy.so/ai-glossary/self-learning-loop): A self learning loop is a process that uses feedback and outcomes to improve future system behavior over time.
- [Synthetic Data](https://www.luffy.so/ai-glossary/synthetic-data): Synthetic data is artificially generated data designed to resemble real-world data for testing, training, or analysis.
- [Task Decomposition](https://www.luffy.so/ai-glossary/task-decomposition): Task decomposition is the act of breaking a complex objective into smaller steps that can be planned, completed, and checked.
- [Tokenization](https://www.luffy.so/ai-glossary/tokenization): Tokenization is the way an AI language model splits text into smaller units it can process.
- [Traceability](https://www.luffy.so/ai-glossary/traceability): Traceability is the ability to connect an AI result or action back to its source data, instructions, tools, and approvals.
- [Training Data](https://www.luffy.so/ai-glossary/training-data): Training data is the collection of examples used to teach a machine-learning model patterns and relationships.
- [Transformer Models](https://www.luffy.so/ai-glossary/transformer-models): Transformer models are neural-network architectures that are especially effective at processing sequences such as language.

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