A multicloud experiment in agentic AI: Lessons learned

Recently I undertook a project to design and validate agentic AI architectures capable of operating autonomously across various public cloud providers. It served as a dry run to ensure I could create these architectures for my clients, test their viability, and refine best practices for multicloud agentic AI deployments.I’ve designed agentic AI systems before but in contained or hybrid environments. This time, I focused solely on using public cloud providers to see how well these platforms would support a decentralized decision-making AI. The system needed to analyze real-time availability, cost, performance, and other factors to dynamically allocate workloads across different clouds and ensure scalability, fault tolerance, and efficiency.Beyond being a technical experiment, this project was an invaluable learning experience. I tested the limits of today’s cloud technologies, confronted practical challenges in cross-cloud orchestration, and honed adaptive design patterns. This project solidified the foundational strategies for developing autonomous, multicloud AI solutions, and I plan to share the lessons I learned with clients and colleagues to help them create their own intelligent agentic systems. Here’s how I approached the experiment, the tools and techniques I used, the obstacles I faced, and the outcomes.

System requirements

At its core, an agentic AI system is a self-governing decision-making system. It uses AI to assign and execute tasks autonomously, responding to changing conditions while balancing cost, performance, resource availability, and other factors. I wanted to leverage multiple public cloud platforms harmoniously. The architecture would have to be flexible enough to balance cloud-specific features while achieving platform-agnostic consistency. The framework would be able to:

Dynamically allocate workloads to the most suitable cloud provider based on real-time analysis

Maintain fault-tolerant processes by rerouting assignments during a failure or slowdown

Operate distributed elements with seamless communication and data flow between components hosted across different cloud platforms

Architectural components

I’m not going to mention the specific cloud providers I used or their specific tools. I do not want this to become a vendor shootout or have the core purpose of the experiment overshadowed by individuals promoting their preferred provider or tool set.

I also do not want my inbox to fill up with email from PR people upset that their clients were not considered, or frustrated if my findings do not flatter their clients’ technology. After more than 30 years of being a tech pundit and influencer, I’m a bit wary of such responses to my work. It misses the point of why I do these exercises. With that said, let’s get started.

The decision-making layer was the heart of the system. It analyzed resource metrics such as latency, cost, throughput, and storage availability. Based on these inputs, it decided where to route workloads or execute tasks. This autonomous layer was designed to:

Assess the current state of resources across clouds

Prioritize tasks and allocate them to the most appropriate environment

Detect issues (e.g., bottlenecks or service failures) and adapt in real time

These goals were achieved by implementing modular AI capabilities that could dynamically assess cloud environments and adjust resource allocation. The workloads had to be containerized and portable, ensuring they could run on different platforms without modification.

An orchestration layer was essential to deploy, scale, and manage these containers across clouds. The orchestration system would:

Deploy workloads based on AI-generated decisions

Monitor resource usage and performance to refine the AI’s decisions

Automatically scale to accommodate fluctuating workloads across environments

A communication layer allowed services running in different clouds to interact seamlessly and ensured effective coordination across environments. Data consistency across providers was maintained via distributed storage mechanisms, where data was replicated, cached, or synchronized depending on use-case requirements.

A monitoring and observability framework allowed the system to function autonomously. As real-time visibility into performance was critical, the observability layer tracked several metrics and fed this information back into the core AI system to improve decision-making over time. This layer collected data on:

Task execution performance

Cloud-specific anomalies or bottlenecks

Cost trends and resource consumption across all environments

The development process

The first step was to provision infrastructure across several cloud providers. Using an infrastructure-as-code approach, I deployed virtual networks, container orchestration environments, and storage solutions in each platform. Achieving connectivity between these environments required careful networking, such as configuring secure tunnels and peering connections to enable low-latency, cross-provider communication.

The AI core needed to be both intelligent and adaptable. I trained the models on simulated resource data to ensure they could make reliable decisions about workload routing. Deploying the AI logic as light, stateless services ensured scalability and allowed easy updates when models evolved.The orchestration layer was tightly integrated with the AI core to enable dynamic decision-making. For example, when faced with heavy demand, the system could spin up additional resources in one cloud to offset latency in another. Likewise, workloads were seamlessly routed to alternate locations if one provider encountered downtime.One of the most critical stages was stress-testing the system. I simulated everything from partial outages to full platform failures. For example, when a server cluster in one cloud went offline, the system redirected processing jobs to resources in another without losing data or state. These scenarios exposed weaknesses, such as inconsistent response times during failover, which I remediated by optimizing workload reprioritization.

Challenges and solutions

Connecting workloads across clouds presented significant hurdles. Latency, security, and compatibility issues required fine-tuning network architectures. I implemented a combination of secure tunnels and overlay networks to improve data exchange reliability.

Tracking costs across clouds was another challenge. Each provider’s billing models were unique, making predicting and optimizing expenses difficult. I integrated APIs to pull real-time cost data into a unified dashboard, which allowed the AI system to include budget considerations in its decisions.Cloud-specific variances sometimes caused misalignments, despite efforts to standardize deployments. For example, storage solutions handled certain operations differently across platforms, leading to occasional inconsistencies in how data was synchronized and retrieved. I resolved this by adopting hybrid storage models that abstracted platform-specific traits.Autoscaling wasn’t consistent across environments, and some providers took longer than others to respond to bursts of demand. Tuning resource limits and improving orchestration logic helped reduce delays during unexpected scaling events.

Key takeaways

This experiment reinforced what I already knew: Agentic AI in multicloud is feasible with the right design and tools, and autonomous systems can successfully navigate the complexities of operating across multiple cloud providers. This architecture has excellent potential for more advanced use cases, including distributed AI pipelines, edge computing, and hybrid cloud integration.

However, challenges with interoperability, platform-specific nuances, and cost optimization remain. More work is needed to improve the viability of multicloud architectures. The big gotcha is that the cost was surprisingly high. The price of resource usage on public cloud providers, egress fees, and other expenses seemed to spring up unannounced. Using public clouds for agentic AI deployments may be too expensive for many organizations and push them to cheaper on-prem alternatives, including private clouds, managed services providers, and colocation providers. I can tell you firsthand that those platforms are more affordable in today’s market and provide many of the same services and tools.This experiment was a small but meaningful step toward realizing a future where cloud environments serve as dynamic, self-managing ecosystems. Current technologies are powerful, but the challenges I encountered underscore the need for better tools and standards to simplify multicloud deployments. Also, in many instances, this approach is simply cost-prohibitive. What’s my overall recommendation? This is another “it depends” answer that people love to hate.