Companies everywhere, from big banks to new startups, are facing a major shift in technology as they work to transform into an agentic enterprise – an organization that uses AI agents to do work alongside humans. The main question on the minds of tech leaders isn’t about choosing the right tools for today; it’s about building systems that can change easily as AI keeps advancing.
The convergence of AI adoption and infrastructure modernization has revealed a clear trend: organizations that remain competitive in this new landscape share common architectural principles. They build their systems so that all the different parts can easily connect and work together. They also create systems that can react instantly to new information and build networks that are centered around the large amounts of data that modern AI needs.
Composable Infrastructure: A New Way to Build
AI capabilities are constantly improving, and new paradigms are emerging monthly. Because of this, the old way of building large, single systems that are hard to change has become a problem. This is leading to what the industry is calling “composable infrastructure,” where systems are built like LEGO blocks—modular and easy to swap out. This gives companies more adaptability as AI evolves.
The main idea behind a composable infrastructure is to build systems from small, interchangeable parts. This is very different from the huge, single systems of the past, which were difficult to change and scale. This shift isn’t just a technical one; it’s a new way of thinking that allows for quick and easy changes in a rapidly changing AI landscape.
One key rule of this approach is to start with APIs. Every service—whether it’s for data, an AI model, or a user’s experience—is treated like a building block with a clearly defined API. This allows different teams, from engineers to outside partners, to build and customize apps independently without getting in each other’s way. The API-first approach ensures that the platform is open and works seamlessly with other cloud-based and on-premise systems.
Another important element is an event-driven architecture. In the age of AI agents, systems need to react instantly to changes. By having services communicate with each other in an indirect way, the system becomes more flexible and can handle sudden spikes in usage. Many organizations use real-time event streams built on Apache Kafka, which helps systems communicate easily and reliably, even when the workload is unpredictable.
The Growing Importance of a Data-Centric Network
We are also seeing a big increase in how much data is processed and moved, especially for training AI models and using them in real-time. This requires faster speeds and more stable network infrastructure.
As customer expectations evolve alongside rapidly advancing technologies, forward-thinking companies are completely rethinking their platforms to support the next generation of applications and solutions. For example, the move to cloud-native architectures has made it possible to deploy technologies flexibly across different public clouds, which helps with scaling and global reach. This flexibility is key for AI at the “edge,” which needs to process data closer to where it’s created to reduce delays.
Leading platforms ensure strong network security by leveraging cloud-native designs that segment infrastructure into discrete, secure functional domains. Each domain is protected by strict controls and completely isolated from others. By using a “Zero Trust” approach, these systems require rigorous identity checks and only give the minimum necessary access during every interaction. Together, these security measures create a secure environment that protects both the platform and sensitive customer data from new threats.
The Path Forward
The journey to becoming an agentic enterprise isn’t about a single tool or technology. It’s about a fundamental shift in how we think about building technology. It’s about creating an infrastructure that isn’t just scalable, but also flexible and strong by its very nature.
Key infrastructural principles for companies to consider as they undergo agentic transformation include:
- Build a flexible and composable architecture that can adapt to AI’s changing needs.
- Prioritize APIs and event-driven architectures to handle the increased communication and data flow.
- Use low latency edge networks to handle more complex processing closer to the data source.
- Don’t underestimate the importance of security, especially in a distributed environment.
- Most importantly, remember that AI is not just about technology; it’s about data. Make sure your network is designed with data at its center.
By embracing these fundamentals, enterprises can build infrastructures that are not just capable of handling today’s AI workloads, but are also adaptable enough to scale an agentic enterprise.
