Coding Agent Control Gates

Teams using Claude Code, Cursor, Copilot instructions, Aider, Codex, or Windsurf should put agent rule files under the same kind of change control used for CI and deployment policy. A practical first build is a small CI gate that hashes agent config files, writes a lockfile, requires review on prompt or tool-permission changes, and blocks risky shell commands or write paths before an agent run starts.

Rel(AI)Build gives a concrete shape for this control layer. It treats prompts, permissions, and workflow state as managed artifacts with SHA-256 addressing, HMAC-stamped lockfiles, hash-chained JSONL audit logs, permission tiers, and pre-tool checks. The corpus result explains why this is worth doing: in 10,008 public GitHub repositories, 10.1% of tracked agent config paths were exact duplicates after fork adjustment, and 75.5% of duplicate clone pairs crossed organization boundaries. These files are already moving like shared software components, but many teams still review them like ordinary markdown.

Engineering teams running repair agents can add a cheap execution policy before building more test infrastructure: start with no execution or a small quota, require the agent to justify each test run, and log whether the run changed localization, patch content, or official evaluation outcome. The goal is to reserve expensive repository setup and repeated test runs for cases where execution changes the decision.

The SWE-bench execution study gives a usable baseline for this policy. Across 7,745 public traces, agents ran tests 8.8 times per task on average. In 3,000 controlled repair attempts, commercial agents gained only 1.25 percentage points in resolve rate under unrestricted execution, with no statistically significant gap. Claude Code resolved 63% without execution and 64% with unrestricted execution, while the no-execution setting saved 56% of tokens and 48% of wall-clock time. A team can test this internally by replaying recent agent tasks under prohibited, quota-1, quota-3, and unrestricted modes, then comparing resolve rate, token use, elapsed time, and mismatches between agent-run validation and the project’s real CI.

Cloud security teams using LLMs to repair Terraform findings should add a layered merge gate for generated patches. The gate should rerun the targeted Checkov check, run full Checkov, run terraform validate, run terraform plan, compare the JSON plan with the original security intent, and send ambiguous plan changes to a human reviewer with labels such as intended fix, deceptive fix, or invalid repair.

TerraProbe shows why a single scanner-cleared result is too weak for Terraform. In its first-pass repair study, Gemini cleared the targeted Checkov finding in 83.3% of repairs, but full Checkov cleanliness fell to 10.4%. Among plan-compared real-world TerraDS repairs, 71.4% were deceptive fixes that passed automated checks while leaving the targeted vulnerability in place. IAM cases were especially concrete: wildcard Resource grants were preserved in all nine CKV2_AWS_11 deceptive-fix cases. A team can start with the highest-risk Checkov rules and measure how often scanner-cleared LLM patches change the actual cloud plan in the intended way.