Enterprise Architecture & AI Glossary

Agentic AI refers to AI systems that pursue goals autonomously by planning multi-step tasks, calling tools and APIs, and adapting based on results. Unlike single-prompt chatbots, agentic systems need deliberate architecture for orchestration, memory, guardrails, and human oversight before they can run safely in an enterprise.

An AI readiness assessment is a structured evaluation of whether an organization's data, platforms, skills, and governance can support AI in production. It identifies gaps and produces a prioritized adoption roadmap, so AI investments target use cases the enterprise can actually deliver and scale.

API-first design is an approach where the API contract is designed and agreed before any implementation is written. Treating APIs as products makes systems easier to integrate, test in parallel, and reuse across teams, channels, and partners.

Architecture-first engineering is the practice of defining system structure, boundaries, and quality attributes before writing code. Making key decisions early reduces rework, delivery risk, and long-term technical debt, especially for AI and platform programs that are expensive to restructure later.

An Architecture Review Board is a governance body that reviews significant technology decisions against enterprise standards, principles, and target-state architecture. An effective ARB speeds delivery by giving teams clear guardrails and fast decisions rather than acting as a late-stage approval gate.

Cloud migration is the process of moving applications, data, and infrastructure from on-premises environments to cloud platforms such as AWS, Azure, or Google Cloud. Common strategies include rehosting, replatforming, and refactoring, chosen per workload based on cost, risk, and modernization goals.

Data architecture is the blueprint for how an organization collects, stores, integrates, governs, and serves data. A sound data architecture, whether a warehouse, lakehouse, or data mesh, is the foundation for reliable analytics and production AI.

Digital transformation is the redesign of business processes, products, and operating models using modern technology. Successful programs are anchored in a target architecture and measurable outcomes rather than isolated tool adoption.

Enterprise architecture is the discipline of aligning an organization's business strategy, applications, data, and technology into a coherent current-state and target-state blueprint. It guides investment decisions, reduces duplication, and gives leaders a roadmap for modernization and AI adoption.

Event-driven architecture is a design pattern where services communicate by producing and reacting to events, typically through a broker such as Apache Kafka. It decouples systems, enables real-time processing, and lets new consumers be added without changing existing producers.

Generative AI is a class of AI models, including large language models, that create new text, code, images, or other content from learned patterns. Enterprise GenAI typically combines models with retrieval-augmented generation (RAG), evaluation, and governance to produce accurate, auditable results.

A Global Capability Center is an offshore or nearshore entity that a company owns or operates to deliver engineering, technology, and business capabilities. GCCs in India give enterprises access to large engineering talent pools, around-the-clock delivery, and full IP ownership.

Microservices architecture structures an application as a set of small, independently deployable services, each owning a single business capability and its data. It improves scalability and team autonomy, but requires mature practices in observability, API design, and distributed data management.

Platform engineering is the discipline of building internal developer platforms that provide self-service infrastructure, golden paths, and shared tooling. A well-designed platform reduces cognitive load on product teams and increases delivery speed while keeping security and compliance consistent.

Serverless architecture is a cloud model where the provider manages servers and scaling, and code runs as on-demand functions or managed services billed per use. It suits event-driven and variable workloads, but needs careful design for cold starts, state, and vendor lock-in.

A software-defined vehicle is a vehicle whose features and functions are primarily delivered and updated through software rather than fixed hardware. SDVs depend on centralized compute, service-oriented vehicle architecture, and secure over-the-air (OTA) updates.

Solution architecture translates a specific business problem into a technical design covering components, integrations, data flows, and non-functional requirements. It bridges enterprise-level standards and the delivery team's implementation choices.

Zero trust architecture is a security model that assumes no user, device, or network is trusted by default and verifies every access request continuously. It relies on strong identity, least-privilege access, micro-segmentation, and continuous monitoring.