Systems are moving from digitization toward architectures that orchestrate data, applications, and AI agents. This evolution places data at the center, enabling an agentic enterprise where autonomy and control coexist: agents execute work at speed, orchestration enforces policy and observability, and applications surface only when human judgment or actions are required.

In this modern design, a unified architecture coordinates every component so that data, automation, and oversight work together to deliver timely, context-rich outcomes. This shift is not just technical – it’s a new way of designing how work gets done and how value is created.

How the System Works

Every process begins with an initiator – whether a user, a system, or an AI agent. The orchestration layer receives the request, evaluates context and dependencies, and determines the best course of action. It may invoke an application capability, retrieve or analyze data, or call on an agent to reason or act. Orchestration ensures each component is engaged at the right moment and that results are returned efficiently and in context. This seamless coordination is what gives the system its intelligence.

High-Level Interaction Flow on How Users, Orchestration, and Components Connect (Figure 1)

Orchestration – The Control Plane with Built‑In Observability

Orchestration serves as the command hub for requests, dependencies, and context. A defining capability is observability – the ability to monitor and trace every interaction and decision across the system. With observability, every request, data flow, and agent action is visible and auditable, enabling transparency and accountability across the architecture.

Integration with applications and external systems occurs through Application Programming Interfaces (APIs), while AI agents are equipped with the right data, context, and parameters via the Model Context Protocol (MCP). Agent to agent (A2A) collaboration supports complex, multi-step workflows.

Because observability is embedded end-to-end, orchestration can track the health, performance, and outcomes of these interactions – making it possible to diagnose challenges, optimize processes, and continuously improve system behavior.

What Sits Behind the Flow

• Applications are organized into modular, reusable layers – interface, API, logic, and storage – so capabilities can be composed and evolved independently.
• Data moves through its own lifecycle – sourced, acquired, managed, and used – ensuring information is trusted, governed, and available when and where it’s needed.
• AI agents operate by setting goals, forming plans, and taking action, all within a system designed for transparency, control, and improvement.

Detailed Interaction Flow on the Structure of the Components (Figure 2)

The Shift from Application-Centric to Data‑Driven

For decades, applications were the primary gateway to business processes. As organizations scaled, many systems became monolithic – bundling transactional logic, reporting, transformations, and workflows into hard-to-change structures. A data-centric approach rebalances this: applications remain important, but data becomes the foundation, agents become the primary actors, and orchestration governs the flow among them.

The companion article, “Application Modernization – A Data-Centric Perspective” recently released, outlines practical steps:

1. Slim the application by externalizing complex rules, transformations, and reporting so it can focus on its core system of record role.
2. Improve data entry by using AI to extract and structure information from emails, documents, and other unstructured sources – reducing manual effort and error.
3. Reassess the application’s purpose – Retire or reimagine legacy systems once their functions are absorbed by modular data services and agent driven workflows.

The result is a lighter application layer supported by a robust, scalable data foundation – one that’s ready for AI‑driven operations.

The Road Ahead

The future favors systems that are agentic and data-native, coordinated by orchestration and grounded in observability. Applications remain part of the story but no longer carry the entire narrative.

By aligning stakeholders around the system’s flow (Figure 1) and structure (Figure 2), and pairing this with the data-centric modernization steps, organizations gain a clear path forward: streamline applications, harness data as a strategic asset, empower agents, and unify operations through orchestration.