Agent-Oriented Terraform#

Most Terraform code is written by humans for humans. It favors abstraction, DRY principles, and deep module nesting — patterns that make sense when a human maintains a mental model of the codebase. Agents do not maintain mental models. They read code fresh each time, trace references to resolve dependencies, and reason about the full resource graph in a single context window.

The patterns that make Terraform elegant for humans make it expensive for agents. Deep module nesting multiplies the files an agent must read. Variable threading through three layers of modules hides dependencies behind indirection. Complex for_each over maps of objects creates resources that are invisible until runtime. The agent spends most of its context on navigation, not comprehension.

Infrastructure Knowledge Scoping for Agents#

An agent working on infrastructure tasks needs to operate at the right level of specificity. Giving generic Kubernetes advice when the user runs EKS with IRSA is unhelpful – the agent misses the IAM integration that will make or break the deployment. Giving EKS-specific advice when the user runs minikube on a laptop is equally unhelpful – the agent references services and configurations that do not exist.

Progressive Agent Adoption#

Nobody goes from “I have never used an agent” to “my agent runs multi-hour autonomous workflows” in one step. Trust builds through experience. Each successful task at one level creates confidence to try the next. Skipping levels creates fear and bad outcomes — the agent does something unexpected, the human loses trust, and adoption stalls.

This article maps the adoption ladder from first task to autonomous workflows, with concrete examples of what to try at each level and signals that indicate readiness to move up.