Dive into the minds of our integration architects and analysts.
Why enterprise architects must design for constant AI-driven change, not just manage it.
A strategic framework to define your organization’s AI ambition across adoption, autonomy, agency, and process coverage.
From fragmented signals to confident decisions in an AI-driven future.
In our Applied Observability Maturity Model, we map maturity across five levels and six key pillars, grounded in real customer environments, evolving technology and strategic business outcomes. Beneath that sits another layer: our Observability reference framework.
The digital world is being reshaped by AI, and that makes it difficult to understand the implications it has on security. AI agents interpret intent, reason over context, plan next steps, and execute actions across tools and systems without a predetermined script. That autonomy forces a different security mindset.
From resources to functions to graphs to goals. How MCP, A2A, and ACP turn APIs from data pipes into capabilities that agents can discover, coordinate, and act on.
From syntactic contracts to cognitive transparency. The seven stages of API maturity in the agentic era, and where MCP and A2A actually fit.
From syntax to semantics. How to design APIs that AI agents can trust, by exposing intent and meaning, not just endpoints and fields.
Central data governance is necessary, but it can’t scale fast enough for an AI-driven enterprise. Why the real key to semantic interoperability sits at the edge, with the systems and teams that create the data.
APIs and AI together: today’s team-up, tomorrow’s game-changer
Observability is more than dashboards and logs. It’s a foundation for shaping your IT landscape and driving better decisions.
Events aren’t just APIs, but should still be managed the same.
Grow your API maturity one stage at a time.
You’re closer than you think to joining a Great Place to Work and a Best Workplace!
