In cloud architectures, best practices around proscriptive environment configurations are routinely automated to ensure that critical factors, like sizing, cost, performance and security, are embedded through infrastructure-as-code by default and by design. CI-CD pipeline deployment automation enforces blue-green deployments to test assumptions, reduce risks associated with defect resolution and limit the “blast radius” of defective features through targeted and limited, soft-launch audiences. Simulation in some form is already used as a routine practice today to provide observability metrics and use those metrics to manage very specific, risk-sensitive, governed outcomes.
More sophisticated simulation models and testing, however, are also not new to IT, software engineering or enterprise software portfolios. We’ve seen attempts to manage and optimize workflows with robotic process automation, business process management and business process optimization. These sometimes fragmented and disconnected solutions mostly facilitate an understanding of current processes and models and help us optimize or refactor processes through brittle, scripted implementation.
The missing piece was not the ability to model and simulate, it was the intelligence automation to analyze, execute and adapt. Enter LLMs, agentic AI and what is now mature digital twin technology for comprehensive simulation of process, system, technologies and ecosystems. The term “digital twin” was originally used by Dr Michael Grieves at the University of Michigan in 2002, during a presentation on product lifecycle management (PLM), so while it found a home in industrial settings, the concept of a mirrored instance to validate design assumptions, risk and reward has broad applications. Further, the marriage of agentic AI with digital twin technology poses an interesting opportunity. But first, let’s look at the respective capabilities of both technologies.
