Building a Visual Approach to Creating Microservices (Free Webinar)

To successfully scale agentic AI, enterprises need to approach it as a systems engineering problem, not just a model deployment exercise.

The network demands of today’s distributed AI workloads was apparent throughout this week’s annual Global NaaS Event (GNE).

Continued investment in the areas of APIs and automation will be the key for many companies to navigate 2023 and beyond.

Embracing an API-forward approach to digital transformation is one of the best ways to maintain business agility and scalability.

Modern enterprise applications, especially those driven by real-time data, AI inference, and latency-sensitive user experiences, are exposing the limitations of a one-size-fits-all cloud strategy, forcing organizations to rethink workload placement across hybrid and distributed environments. The shift is no longer about “cloud vs. on-prem,” but about architecting for performance, cost predictability, and data gravity in a world where systems must operate continuously and adapt dynamically.

The AI revolution isn’t happening in GPUs or model architectures. It’s happening quietly in the data pipelines that feed them.

The future of AI in the enterprise is recognizing that intelligence, whether human or artificial, must be governed by clear, written code.

Graph technology expert Dominik Tomicevic on why Anthropic’s model context protocol offers a much stronger way for LLMs to connect with the outside world than previous approaches, but it needs the helping hand of graph technology and GraphRAG.