Pushing Boundaries: Building Agentic AI Systems Across Multiple Clouds
In a recent initiative, I set out to explore whether agentic AI architectures could function autonomously across several public cloud platforms. This wasn’t just a technical proof-of-concept—it was a strategic rehearsal to refine methodologies I could confidently bring to client projects. The objective was to validate that these intelligent, self-directing systems could not only survive but thrive in multicloud environments, adapting in real time to changing resource landscapes and constraints.
While I’ve worked on agentic systems in more controlled, hybrid setups before, this effort was different in scope and ambition. I focused exclusively on public cloud infrastructure, challenging myself to build an AI that could orchestrate itself across cloud providers without centralized oversight. The system had to ingest real-time data on cost, latency, availability, and workload demand—and then autonomously determine where and how to execute its tasks. It was designed to be flexible, resilient, and cloud-agnostic, while still taking advantage of provider-specific strengths when it made sense.
The real value, however, went beyond architecture. This experiment tested the current capabilities of multicloud environments and surfaced some of the messier realities of distributed orchestration—like inconsistent APIs, latency across regions, and the quirks of cross-cloud communication. Through that process, I developed a stronger set of adaptive design patterns and operational principles tailored to autonomous systems navigating heterogeneous infrastructures. These lessons are already informing my client recommendations and shaping more resilient deployment models going forward.
At the core of this architecture lies a simple but powerful principle: autonomy through informed decision-making. The agentic AI system continuously evaluates cloud options to allocate workloads intelligently, reroutes processes in the face of disruptions, and keeps distributed components in sync across providers. The design demanded a modular, event-driven framework capable of evaluating trade-offs in real time. It wasn’t just about spinning up workloads; it was about orchestrating them with purpose. The results? A robust, fault-tolerant prototype that proved multicloud agentic AI isn’t just possible—it’s practical with the right approach.