Glossary - AI Squared

A

A2A (Agent-to-Agent)

Agent-to-Agent communication allows autonomous AI systems to exchange information, coordinate tasks, and collaborate toward shared goals without human intervention.

Act (Assist–Augment–Act)

The Act stage represents full AI autonomy, where systems independently complete tasks using reasoning and adaptation, enabling efficient end-to-end decision execution.

ADVANA (DoD Advancing Analytics)

ADVANA is the DoD’s enterprise analytics platform that aggregates data across systems to support scalable analysis, informed decisions, and mission-critical insights.

Agent Orchestrator

An agent orchestrator coordinates multiple AI agents, assigning tasks, synchronizing actions, and ensuring smooth collaboration across complex workflows and objectives.

Agent Reasoning

Agent reasoning enables AI to break down problems, evaluate context, plan steps, and adapt based on outcomes, supporting accurate and autonomous decision-making.

Agent Washing

Agent washing refers to overstating simple automation as advanced agentic AI, misleading users about true autonomy, reasoning, and adaptability in AI-driven systems.

Agentic AI

Agentic AI combines reasoning, planning, memory, and autonomy to pursue goals independently, enabling proactive decision-making across dynamic enterprise workflows.

Agentic Applications

Agentic applications use autonomous AI to make decisions and adapt in real time, allowing systems to handle complex workflows with minimal human involvement.

Agentic RAG

Agentic RAG enhances retrieval-augmented generation by adding autonomy and reasoning, allowing AI to decide what to retrieve, interpret, and act on dynamically.

Agentic Workflows

Agentic workflows enable AI agents to plan and execute tasks independently, adjusting to context and changes to deliver efficient, adaptive operational outcomes.

AGI (Artificial General Intelligence)

AGI represents a theoretical form of AI capable of learning, reasoning, and performing any intellectual task across domains at human-level adaptability.

AI (Artificial Intelligence)

Artificial Intelligence refers to systems that perform tasks requiring human-like reasoning, learning, and perception, supporting automation and decision-making.

AI Safety

AI safety ensures intelligent systems operate securely, ethically, and reliably, minimizing risks like bias, misuse, or unintended outcomes in sensitive environments.

AI Workflows

AI workflows structure how models gather data, reason, and act within applications, enabling automated, context-aware processes that assist or complete tasks.

API (Application Programming Interface)

APIs enable software systems to communicate and share data, forming the backbone of integrating AI models, tools, and applications in enterprise environments.

Assist (Assist–Augment–Act)

The Assist stage provides insights and information to users without driving actions, supporting decision-making while keeping control entirely with humans.

Augment (Assist–Augment–Act)

Augment describes AI that enhances human work by offering recommendations, predictions, or guided actions to increase accuracy and efficiency in workflows.

Augmentation

Augmentation enriches AI models with domain-specific data to improve accuracy, relevance, and contextual understanding without retraining the underlying model.

Autonomous Agents

Autonomous agents plan, act, and adapt independently to achieve defined goals, handling multi-step tasks and learning from outcomes to improve performance.

B

Business Context Capture

Business context capture collects relevant operational data to ground AI decisions, ensuring outputs reflect real situations and support accurate, timely actions.

C

Chain-of-Thought (CoT) Prompting

Chain-of-thought prompting guides AI to reason step-by-step, improving transparency, accuracy, and logical consistency in complex or multi-stage decisions.

Chunking

Chunking breaks long documents into manageable segments for efficient retrieval and processing, improving the accuracy and speed of AI-driven information search.

Context Engineering

Context engineering designs how relevant business information is structured and applied in AI reasoning, ensuring outputs align with goals and constraints.

Context Window

A context window defines how much information an AI model can process at once, affecting its ability to understand long conversations or detailed documents.

Conversational AI

Conversational AI enables natural interactions through text or voice, supporting chatbots and assistants that interpret intent and respond meaningfully.

Conversational UI

Conversational UI allows users to interact through natural language instead of menus or buttons, simplifying access to tools and information.

D

Data Apps

Data Apps embed AI insights directly into business applications, enabling real-time visualization, interaction, and feedback without heavy engineering effort.

Data Governance

Data governance establishes rules and processes that maintain data accuracy, security, and compliance, ensuring reliable foundations for AI and analytics.

Data Movement

Data movement transfers information across systems to ensure availability for analysis, AI modeling, and operational workflows in real time or near real time.

E

Enterprise AI

Enterprise AI delivers secure, scalable, and integrated intelligence across business systems, meeting strict requirements for compliance, reliability, and adoption.

Enterprise RAG

Enterprise RAG grounds generative AI responses in secure internal data with access controls, ensuring accurate, compliant, and audit-ready information retrieval.

ETL (Extract-Transform-Load)

ETL extracts data, refines it, and loads it into target systems, creating structured, consistent datasets for analytics, reporting, and AI workflows.

Explainable AI (XAI)

Explainable AI provides visibility into how models make decisions, improving trust, compliance, and understanding in regulated or high-stakes environments.

F

Feedback Loop

A feedback loop captures user evaluations of AI outputs and uses them to refine models, driving continuous improvement based on real-world performance.

Fine-Tuning

Fine-tuning adapts a pre-trained model to a specific domain by training it on targeted data, improving relevance, accuracy, and contextual understanding.

Foundation Models

Foundation models are large AI systems trained on broad datasets that can be adapted to many tasks, forming the base layer for advanced enterprise applications.

FSI (Federal Systems Integrator)

FSIs integrate complex federal systems, coordinating data, tools, and vendors to deliver mission-aligned, compliant, and scalable government solutions.

G

Generative AI

Generative AI creates new content, from text to images based on learned patterns, helping automate writing, summarization, support, and creative tasks.

GPL (General Public License)

The GPL is a copyleft license that ensures derivative software remains open-source, preserving user freedoms to study, modify, and share code.

GPT (Generative Pre-trained Transformer)

GPT models generate human-like text by understanding context and patterns, supporting tasks like summarization, analysis, and conversational responses.

H

Hallucination (AI Hallucinations)

A hallucination occurs when AI produces incorrect or fabricated information, highlighting the need for grounding, safeguards, and reliable context sources.

I

Ingestion

Ingestion collects and processes data from various sources for indexing, retrieval, or analysis, ensuring AI systems work with current, structured information.

K

Knowledge Base

A knowledge base stores structured and unstructured information that AI can reference, ensuring consistent, accurate, and accessible organizational knowledge.

Knowledge Graph

Knowledge graphs organize entities and their relationships, enabling AI to reason about connections and deliver richer, context-aware insights.

L

Large Language Model (LLM)

LLMs interpret and generate language at scale, enabling summarization, analysis, conversation, and reasoning across diverse enterprise applications.

LLM Orchestration

LLM orchestration manages how models interact with tools, data, and systems, ensuring coordinated actions and accurate, context-aware workflows.

Long-Term Memory

Long-term memory stores information across sessions, allowing AI to recall past interactions, preferences, and decisions for continuity and personalization.

Low-Code / No-Code

Low-code and no-code tools let users build AI workflows without programming, accelerating innovation and reducing technical barriers in organizations.

M

Machine Learning

Machine learning enables systems to learn patterns from data and improve predictions over time, forming the basis for modern AI applications.

MCP (Model Context Protocol)

MCP standardizes how AI connects to tools and data, enabling consistent interactions across applications and improving agent interoperability.

Memory

Memory allows AI systems to retain relevant information for coherent reasoning, supporting personalized experiences and multi-step workflows.

MIT License

The MIT License is a permissive open-source license that allows broad reuse and modification while requiring attribution of the original code.

Multi-Agent Orchestration

Multi-agent orchestration coordinates multiple autonomous agents, enabling parallel task execution and distributed decision-making in complex workflows.

Multi-Agent Systems

Multi-agent systems consist of multiple autonomous units collaborating toward shared goals, improving scalability and efficiency in dynamic environments.

Multi-Vector Search

Multi-vector search uses multiple embeddings to interpret queries, improving retrieval accuracy for complex or ambiguous information requests.

Multimodal AI

Multimodal AI processes inputs like text, images, and audio together, enabling richer understanding and more comprehensive enterprise applications.

N

Natural Language Generation (NLG)

NLG converts structured data into readable text, enabling automated reports, summaries, and personalized communications in enterprise workflows.

Natural Language Processing (NLP)

NLP enables machines to analyze and understand human language, supporting tasks like classification, sentiment analysis, and document processing.

Natural Language Understanding (NLU)

NLU interprets intent and meaning behind text, improving the accuracy of AI assistants, search systems, and conversational interfaces.

O

Ontology

An ontology defines concepts and relationships within a domain, helping AI reason more accurately and maintain consistent understanding across systems.

Open-Source LLM

Open-source LLMs provide transparent, modifiable models that organizations can deploy securely and customize for domain-specific intelligence.

P

Prompt Chaining

Prompt chaining links multiple AI prompts to handle multi-step tasks, improving structure, coherence, and reliability in complex reasoning.

Prompt Engineering

Prompt engineering designs effective model inputs to improve accuracy, relevance, and clarity of outputs without modifying underlying models.

R

RAG (Retrieval-Augmented Generation)

RAG grounds generative models in verified data by retrieving relevant content before producing responses, reducing hallucinations and improving accuracy.

RBAC (Role-Based Access Control)

RBAC restricts access to data and features based on user roles, ensuring secure, compliant, and controlled use of AI systems in enterprise settings.

Reinforcement Learning (RL)

RL trains agents through rewards and penalties, enabling improvement in decision-making tasks where outcomes evolve over time.

Reverse ETL

Reverse ETL operationalizes analytics by sending processed data from warehouses into business tools, making insights actionable in real workflows.

RLHF (Reinforcement Learning from Human Feedback)

RLHF aligns AI outputs with human expectations by using user ratings to refine behavior, improving safety and accuracy in model responses.

Robotic Process Automation (RPA)

RPA automates rule-based tasks by mimicking user actions, improving speed and consistency for repetitive processes within structured workflows.

S

SBIR (Small Business Innovation Research)

SBIR funds small businesses developing innovative technologies, supporting feasibility, development, and commercialization for mission-aligned solutions.

Short-Term Memory

Short-term memory maintains session context enabling coherent AI reasoning without long-term retention.

SLA (Service Level Agreement)

An SLA defines performance expectations, responsibilities, and service commitments, ensuring reliability and accountability between providers and clients.

Small Language Models (SLMs)

SLMs are efficient, lightweight models optimized for domain-specific tasks, enabling fast inference in secure or resource-limited environments.

Sparx

Sparx connects to enterprise systems to deliver instant, context-aware answers, reducing reporting burdens and accelerating decision-making for teams.

STTR (Small Business Technology Transfer)

STTR funds small businesses collaborating with research institutions to advance early-stage technologies toward practical deployment.

Supervised Learning

Supervised learning trains models using labeled data, enabling accurate predictions for tasks where outcomes are well-defined and measurable.

T

Transparency

AI transparency explains how models make decisions and use data, helping organizations improve trust, accountability, and compliance in AI-driven systems.

U

Unifi

Unifi embeds AI insights directly into business applications through no-code integration, enabling real-time decision support and accessible enterprise intelligence.

Unstructured Data

Unstructured data like text and images requires AI-driven extraction and processing to generate usable enterprise insights and support informed decision-making.

Unsupervised Learning

Unsupervised learning identifies patterns and clusters in unlabeled data, enabling organizations to uncover insights and structure information without manual tagging.

V

Vector Database

Vector databases store high-dimensional embeddings to power fast semantic search, supporting RAG systems, recommendations, and contextual enterprise intelligenc

Vector Search

Vector search compares embeddings to deliver accurate, intent-aware retrieval, improving how enterprises access relevant information across large datasets.

AI Squared Glossary: Your Guide to Data, AI & Agentic Workflows in the Enterprise