Agentic AI refers to artificial intelligence systems that can plan tasks, make decisions, and take actions toward goals with limited human intervention.
API-first architecture is an application design approach where application functionality is exposed and accessed through well-defined APIs before other system components are built.
Application integration is the process of enabling different software systems to communicate, exchange data, and operate together as part of a coordinated technology environment.
Application modernization is the process of updating legacy software systems so they can operate effectively in modern technology environments.
Artificial intelligence (AI) is a field of computing focused on building systems that can analyze data, recognize patterns, and support or automate decisions that traditionally required human judgment.
Cloud architecture is the design framework that defines how applications, data systems, and infrastructure are organized and operated within cloud environments.
Cloud computing is a model for delivering computing resources—such as storage, processing power, and applications—over the internet instead of on local infrastructure.
Cloud migration is the process of moving applications, data, and infrastructure from on-premise systems or legacy environments into cloud platforms.
Cloud-native architecture is an approach to designing applications specifically for cloud environments using distributed, scalable, and resilient system principles.
Data architecture is the framework that defines how data is structured, stored, integrated, and managed across an organization’s systems and platforms.
Data engineering is the discipline of designing, building, and maintaining systems that collect, process, store, and deliver data for analytics, applications, and artificial intelligence.
Data migration is the process of transferring data from one system, platform, or environment to another while maintaining accuracy and integrity.
Data modernization is the process of upgrading legacy data systems, architectures, and practices to support scalable analytics, cloud environments, and artificial intelligence.
A data platform is a centralized environment that stores, processes, and manages data so it can be used for analytics, applications, and artificial intelligence.
Deep Learning is a type of machine learning that uses layered neural networks to identify complex patterns in large datasets, particularly in text, images, audio, and other unstructured data.
Digital platforms are technology environments that enable organizations to deliver digital services, connect users, and coordinate interactions between applications, data, and business processes.
Enterprise applications are large-scale software systems designed to support core business operations, processes, and data management across an organization.
Generative AI is a type of artificial intelligence that creates new content—such as text, images, audio, or code—by learning patterns from large datasets.
Hybrid cloud is a computing environment that integrates on-premise infrastructure with public cloud platforms, allowing workloads to operate across both environments.
Machine learning is a method within artificial intelligence that enables software systems to learn patterns from data and improve their predictions or decisions without being explicitly programmed for every scenario.
Software engineering is the discipline of designing, building, testing, and maintaining software systems using structured engineering principles and development processes.
Continuous delivery is a software engineering practice that enables organizations to release application updates frequently and reliably through automated build, test, and deployment processes.
DevOps is an engineering approach that integrates software development and IT operations to improve the speed, reliability, and scalability of software delivery.
Platform engineering is the practice of building and operating internal technology platforms that standardize infrastructure, tools, and workflows used by software engineering teams.
Site Reliability Engineering (SRE) is an engineering discipline that applies software engineering practices to ensure the reliability, scalability, and performance of technology systems.