As businesses adopt a multi-cloud strategy, implementing agentic AI can be a complex challenge. I’d like to explore this topic further and gather insights from those with experience.
In our institution, we have a mix of public GCP and Azure clouds, with client data mostly residing in GCP and colleague data and ops in Azure. There’s a desire to partner with just one cloud provider for agentic AI, but I’m not convinced that’s possible.
So, what are the key considerations for adopting agentic AI in a multi-cloud environment?
## The Complexity of Multi-Cloud
With different cloud providers come different infrastructure, security, and management requirements. Agentic AI, which relies on autonomous decision-making, adds another layer of complexity.
## Cloud Provider Lock-In
Partnering with a single cloud provider might seem like a straightforward solution, but it could lead to vendor lock-in, limiting our flexibility and increasing costs.
## Data Management
Our client data is in GCP, while colleague data and ops are in Azure. How do we manage data across these different clouds while ensuring security, consistency, and compliance?
## Security and Compliance
Agentic AI introduces new security and compliance risks, such as data breaches and unauthorized access. How do we mitigate these risks across multiple clouds?
## Interoperability
To fully leverage agentic AI, we need seamless interoperability between our clouds. What are the best practices for achieving this?
If you’ve tackled similar challenges, I’d love to hear your thoughts and experiences. Let’s explore this topic further and find ways to overcome the hurdles of implementing agentic AI in a multi-cloud environment.
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*Further reading: [Agentic AI: A Primer](https://www.researchgate.net/publication/329514651_Agentic_AI_A_Primer)*