Glossary

Agentic AI is a form of artificial intelligence that acts autonomously to understand goals, plan how to achieve them, and execute the steps with limited human supervision. Systems can employ different agent types as the complexity of tasks increases: simple reflex agents (rule-based reactions), model-based agents (context-aware decisions using internal world models), goal-based agents (planning-focused systems that evaluate action sequences), utility-based agents (optimization-focused evaluators that maximize desired outcomes), and learning agents (adaptive systems that improve from experience). This way, agents function more like experienced teammates, rather than passive tools that need constant human direction.

Agentic AI integration is the process of connecting autonomous AI systems (agents that can reason, plan, and act independently) with your existing enterprise data sources, applications, APIs, and workflows. Unlike traditional automation tools that follow rigid, predefined scripts, these agents execute multi-step workflows, adapt to changing conditions, and integrate directly with ERP, CRM, and cloud platforms to complete complex tasks that were previously the exclusive domain of human intelligence.

AIOps (Artificial Intelligence for IT Operations) applies machine learning, big data analytics, and automation to IT operations. By continuously analyzing telemetry data (logs, metrics, traces, and events) from across the IT environment, AIOps platforms detect anomalies, correlate related events, predict failures, and trigger automated responses, helping teams resolve issues faster and prevent downtime.

An enterprise AI platform is a system of integrated technologies that enables you to build, deploy, and operate Artificial Intelligence (AI) applications across an organization. The purpose is to move from isolated proof-of-concept to production-grade AI systems that connect with your existing infrastructure. The platform equips enterprises with key capabilities, providing shared infrastructure, consistent governance, and standardized development practices that let teams move faster while maintaining control.

Responsible AI is the foundational practice of designing, developing, and deploying artificial intelligence systems that are safe, trustworthy, and aligned with ethical principles and legal standards. This includes the continuous evaluation of AI systems, from initial data sourcing and model development to deployment, monitoring, and ongoing iteration, to ensure they operate reliably and meet your organization's values.

Agentic AI frameworks are development toolkits that enable autonomous AI systems to move beyond request-response patterns into genuine reasoning loops where agents think, act, observe results, and adjust next steps. What makes frameworks “agentic” is their persistent memory, access to external tools and APIs, support for human-in-the-loop review, integrated guardrails, and resilient state management.

Agentic AI security refers to the specialized set of practices, controls, and technologies designed to safeguard autonomous AI agents and agentic systems that can plan, reason, and act across enterprise environments. Unlike traditional AI, agentic models operate independently and actively modify data, call APIs, traverse cloud environments, and coordinate across services and with other agents, creating distinct security challenges that require real-time guardrails, governance, and observability instead of static defenses.​

Agentic AI tools are platforms and frameworks that give AI agents the ability to plan, act, and complete tasks autonomously across enterprise systems. They integrate agents with data and applications, manage state, enforce guardrails, control multi-step workflows, and provide full observability, allowing autonomous agents to operate safely and reliably in production.

AI data integration uses artificial intelligence to automate the discovery, collection, transformation, and unification of data across systems for analytics and AI workloads. AI-powered data integration builds on cloud data platforms, data modernization, and data quality practices by using intelligent methods to infer mappings, flag anomalies, enrich metadata, and adapt pipelines as schemas and sources change. This enables a data-as-a-product approach and keeps analytical and AI services fed with consistent, trustworthy, and well-documented data at enterprise scale.​​

AI-powered enterprise search and discovery use semantic understanding, vector embeddings, generative models, and contextual intelligence to find information from data sources. Unlike traditional keyword-based search, these systems interpret user intent, generate contextual answers with source citations, and integrate with knowledge bases, intranets, e-commerce platforms, and business applications to deliver semantically relevant results that respect existing permission structures.

AI governance defines how autonomous, stateful agents are designed, deployed, monitored, and retired so they remain controllable, auditable, and aligned with business and regulatory constraints across long-running workflows and multiple systems. By integrating agentic AI governance directly into AI lifecycles, organizations address risks like bias, privacy infringement, model drift, and unintended harm through continuous oversight.​

An AI Proof of Concept (AI POC) is a low-risk prototype or controlled experiment of a proposed AI solution that tests whether the solution is technically viable and capable of delivering the intended outcomes. It validates feasibility, confirms data readiness, and measures expected outcomes against predefined success criteria. This prevents AI project failures and expenses.