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 for customer service is the use of autonomous AI agents to orchestrate customer journey workflows across the full engagement lifecycle, from discovery and purchase to support, retention, and account management. These agents collaborate with other agents to interpret what a customer needs, pull context from the systems that hold relevant data, decide on

Agentic AI data engineering uses autonomous, goal-driven enterprise AI agents to design, operate, and improve data pipelines, quality checks, and governance workflows with minimal manual intervention. Instead of only generating code snippets or SQL suggestions, these agents can plan multi-step workflows, call tools, monitor outcomes, and adapt behavior based on feedback. In practice, agentic AI

Agentic AI development is the process of designing, building, integrating, and operating AI agents that can plan tasks, call tools, and complete multi-step work across enterprise systems under clear guardrails. It treats agents as part of production workflows, not standalone demos, and makes sure they connect to real data, follow business rules, and can be

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.

An agentic AI platform is the foundational system that enables enterprises design, orchestrate, deploy, and govern AI agents that plan, reason, act, and collaborate autonomously across tools, data sources, and workflows. It creates an environment where agents understand objectives, break problems into steps, call APIs and enterprise systems, adapt in real time, and escalate decisions

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.