Deterministic Automation for a Probabilistic System
The article discusses the challenges of using deterministic automation in probabilistic AI systems. It highlights the complexities of infrastructure as code (IaC) tools and the learning curve associated with different cloud providers. The introduction of AI agents adds unpredictability to the provisioning process, raising concerns about control and reliability.
- ▪AI agents are probabilistic, meaning they do not always produce the same output for the same prompt.
- ▪Using infrastructure as code tools requires learning both the tool's abstraction and the underlying cloud API, resulting in a 200% learning cost.
- ▪The introduction of AI agents in infrastructure provisioning can lead to unpredictability, as they may not always handle dependencies and operations correctly.
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ai Deterministic Automation for a Probabilistic System AI agents are probabilistic. The same prompt doesn't always produce the same output. That's not a problem to avoid. It's a problem to engineer around. Typed schemas, validated execution, deterministic workflows. The agent reasons freely. The system keeps it honest. Paul Stack 26 May 2026 • 8 min read The complexity is real. The guardrails are the room. I've spent more than a decade building infrastructure tooling. Puppet. Chef. Ansible. CloudFormation. Terraform. Pulumi. Kubernetes. Helm. Argo CD. I didn't just use these tools. I helped build some of them, wrote providers for others, and watched each generation try to solve the problems the last one left behind. Every few years the tooling shifts and we learn the new thing.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at The View from the AI Frontier.
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