NEXUSTEK
GLOSSARY
An explanation of industry terms that is a quick read, and knowledge base.
Agentic AI
Agentic AI is artificial intelligence (AI) able to take a goal or instructions from a person, gather information from different sources, decide what needs to happen next, and take action, all with limited human oversight. The ability to handle multiple steps on its own is a large part of the technology’s appeal, especially when it comes to repetitive, time-consuming work or work that involves coordinating across several systems.
AI
Artificial intelligence (AI) is technology that can perform tasks that typically require human intelligence. It uses techniques such as machine learning (ML) and analytics to recognize patterns, make predictions, generate content, support decisions, and, in some cases, take action. AI includes many different AI technologies, including ML, natural language processing (NLP), computer vision, generative AI (GenAI), and agentic AI, each suited to different kinds of problems and levels of autonomy.
AI Agent
An AI agent is an autonomous software system that uses AI to complete specific tasks or work toward a defined goal. AI agents are typically narrower in scope than agentic AI, which describes the broader ability to coordinate multiple agents, systems, and actions toward an outcome. Think of agents as the individual workers within a larger agentic AI system.
AI Application
An AI application is software that uses artificial intelligence (AI) to provide intelligent advice, automate decisions, generate content, or perform tasks traditionally handled by people. Common examples include virtual assistants, recommendation engines, fraud detection tools, and recruiting applications that screen and rank candidates.
AI Enablement
AI enablement is the process of preparing an organization’s systems, people, and technology to use AI effectively. It includes governance, employee training, system integration, compliance, data preparation, and building the technology foundation needed to support AI initiatives.
AI Model
An AI model is a computer program trained on large amounts of data to recognize patterns, make predictions, or generate content without being explicitly programmed for every outcome. Well-known examples include large language models (LLMs) such as GPT and Gemini.
AI Operations
AI operations refer to the ongoing management of AI systems, models, agents, and applications once they are deployed. This can involve both people and automated tools performing tasks such as monitoring performance, addressing issues, managing model versions and rollbacks, and allocating GPU and compute resources.
AIOps
AIOps, or artificial intelligence for IT operations, uses AI and machine learning to automate and improve IT operations. It combines capabilities such as monitoring, observability, anomaly detection, event correlation, and predictive analytics to reduce manual work and help IT teams respond to problems faster.
Business Continuity
AIOps, or artificial intelligence for IT operations, uses AI and machine learning to automate and improve IT operations. It combines capabilities such as monitoring, observability, anomaly detection, event correlation, and predictive analytics to reduce manual work and help IT teams respond to problems faster.
Cloud Migration
Cloud migration is the process of moving an organization’s data, applications, and workloads from an on-premises infrastructure to a cloud environment. This can include moving to a public or private cloud, or moving workloads between cloud environments. Moving workloads from a public cloud back to private cloud or on-premises infrastructure is commonly known as cloud repatriation. Organizations often migrate to improve efficiency, control costs, modernize technology, and gain access to capabilities such as AI, machine learning, and advanced analytics.
Cybersecurity
Cybersecurity is the protection of technology, systems, networks, and data from threats, attacks, and vulnerabilities. It’s a crucial part of modern business operations and continues to become more challenging as cyber threats evolve. Organizations increasingly take a proactive approach by combining security practices with technologies such as zero trust, advanced threat detection and response, and AI-driven automation to identify and address risks faster.
Deploying AI
Deploying AI is the process of putting an AI model, AI agent, or application into a production environment where people or business systems can use it. It includes activating the technology, providing access to the necessary data, connecting it to other platforms, and configuring it for real-world use. AI deployment is also commonly referred to as implementing, provisioning, or configuring AI.
Disaster Recovery
Disaster recovery (or DR) is part of a broader business continuity strategy, and refers to the processes and policies organizations use to restore IT systems and resume operations after a disruption. Events can include power outages, cyberattacks, hardware failures, or natural disasters. If infrastructure is damaged or unavailable, applications and data can be recovered from backups or replicated environments at another location.
Generative AI (GenAI)
Generative AI (GenAI) is a type of artificial intelligence (AI) that can create new content, including text, images, video, audio, and software code, in response to prompts. Its ability to understand natural language and generate useful content has made it one of the most widely adopted forms of AI.
Hybrid Cloud
A hybrid cloud is a cloud operating model that combines public cloud, private cloud, and on-premises infrastructure, giving organizations flexibility to run and move applications and data across different environments. Organizations can place workloads where they make the most sense based on factors such as performance, cost, security, and compliance, while adding capacity when needed.
Knowledge Base
A knowledge base organizes information so it can be efficiently searched, retrieved, and applied. In AI systems, knowledge bases are often used with retrieval-augmented generation (RAG), where an AI model retrieves relevant information from the knowledge base to help inform and ground the response. Common content types include FAQs, installation guides, glossaries, policies, and workflows.
Large Language Model
A large language model (LLM) is an advanced type of AI model trained on enormous amounts of data to understand and generate human-like language. LLMs can answer questions, summarize information, create content, analyze text, and assist with many other language-based tasks. ChatGPT and Gemini are well-known examples.
Managed Intelligence
Managed intelligence refers to services that continuously manage and improve AI-driven business workflows, including AI agents, applications, and models. It extends traditional managed services by focusing on the performance, reliability, compliance, and ongoing refinement of AI systems after deployment.
Multi-Cloud
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
Private Cloud
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
Public Cloud
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
RAG
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
SaaS (Software as a Service)
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
Workflow
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.
Zero Trust
Multi-cloud refers to using cloud services from more than one cloud provider. A multi-cloud strategy gives organizations the flexibility to choose the environment that best fits each workload based on factors such as performance, cost, capabilities, security, and location. That flexibility can bring additional complexity, making visibility and consistent management across cloud environments important.