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Background. Infrastructure-as-Code (IaC) scanners detect cloud misconfigurations in Terraform and other IaC languages before deployment, but repairing the flagged configurations remains largely manual. Recent Large Language Model (LLM)-based repair approaches can repair some findings, but may hallucinate unsupported constructs or suppress warnings without fixing the issue. Aims. We study whether tool grounding can improve LLM-based Terraform repair, and when a finding should be escalated because the required deployment-specific context is not available. Method. We present TerraRepair, a prototype of a tool-grounded LLM agent for Terraform repair with structured escalation. TerraRepair retrieves dependency context from Terraform references, consults the installed provider schema, and re-runs the scanner before returning a candidate repair. When the required context is absent, TerraRepair escalates instead of fabricating a plausible fix. Results. We evaluate our tool on two vulnerable-by-design Terraform repositories using two IaC security scanners, Checkov and Trivy, across AWS, Azure, and GCP. On the combined AWS benchmark, TerraRepair improves scanner-verified fix rates from 26.6% to 78.4% on Checkov and from 44.8% to 72.4% on Trivy, compared with a controlled one-shot baseline. It also reduces the baseline’s 44.8-73.6 percentage point (pp) claimed-vs-verified repair gap to under 5 pp. In a sampled semantic audit covering AWS only, 78.9% of TerraRepair’s scanner-verified AWS repairs are labeled as correct under a majority-vote protocol with two LLM judges and one author. Conclusions. These emerging results show that tool grounding can substantially improve scanner-verified LLM-based IaC repair on the studied benchmarks, while missing deployment-specific context remains the main knowledge boundary for full autonomy.