Rethinking Enterprise Architecture for Agentic AI

With the rise of agentic AI, it feels like the right time to step back and re-examine how our traditional systems are designed and how they talk to each other.

Most enterprise architectures were built for a very different world: deterministic workflows, batch jobs, and clearly defined hand-offs between systems. We were optimizing for reliability, throughput, and process compliance, and that's how we scaled.

The Agentic Shift

Agentic AI changes that equation.

When systems can reason, plan, and act autonomously, the real bottleneck is no longer workflow orchestration. It’s "time-to-inference". Latency becomes part of the experience. System boundaries turn into cognitive friction. Every extra synchronous call or brittle integration directly affects how “intelligent” the product actually feels to the user.

This shift forces some uncomfortable but necessary questions:

• Are our systems exposing enough context, or just moving data around?
• Are decision paths optimized for speed and relevance, or for completeness?
• Are our most critical experiences still designed around static workflows instead of fast inference loops?

From Process to Context

This doesn’t mean we need to rewrite every "System of Record". In most cases, it means adding an intelligence layer that can reason across systems while keeping core platforms stable, focusing our effort on the experience-critical paths where real-time decisions truly matter.

Agentic AI isn’t just another capability to bolt on. It’s a forcing function to move from process-led automation to context-aware experience-led intelligence.

Curious how others are thinking about this shift, especially in large, legacy enterprise environments.