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A Terraform check can fail while an LLM call still runs if the workflow allows execution to continue. The cause depends on where the LLM call lives: a later step in the same job, a separate job, or an independently triggered workflow. Check the workflow YAML and the run’s condition logs before deciding what happened; the headline alone does not identify the defect.
First establish what “rejected” means
A rejected Terraform change might mean a command returned a failing exit code, a policy check denied a run, or a change is waiting for human approval. These outcomes do not necessarily produce the same workflow behavior. Identify the specific gate and its result before tracing why the LLM ran.
- Command failure: A format check, validation command, plan, or scanner may cause a step to fail by returning a nonzero exit code. GitHub Actions uses exit status to determine whether an action failed; see GitHub’s documentation on setting exit codes.
- Policy result: Terraform policy enforcement has enforcement levels, and some violations may be overridden by users with the appropriate permissions. A policy result is not automatically equivalent to a failed shell command. See HashiCorp’s Terraform policy documentation.
- Terraform conditions: Failed preconditions stop the current operation; failed postconditions halt downstream processing that depends on the resource. Terraform’s configuration-language validation documentation describes these behaviors.
- Approval pause: A request for human approval is a control point, not necessarily a failed command. Determine whether the LLM ran before or after that approval stage.
Locate where the LLM call runs
GitHub Actions treats steps in one job differently from separate jobs. A job is a set of steps on the same runner; separate jobs require dependencies to establish sequencing. An LLM call in another workflow triggered independently may not be governed by the Terraform job’s dependency at all.
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By default, a step runs only if earlier steps have succeeded. But continue-on-error: true can let the job proceed after a step fails, and a step-level condition can explicitly permit later execution. Inspect the Terraform step and the LLM step for continue-on-error, if:, and any references to the Terraform step’s outcome or conclusion.
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HashiCorp’s setup-terraform action documentation shows Terraform format and plan steps configured with continue-on-error: true so a workflow can collect and report outcomes. That pattern is an example, not evidence that it caused a particular incident. When the Terraform wrapper is enabled, the action exposes command output and an exit-code output; use those results to define an explicit gate decision rather than assuming the enclosing job’s success tells the whole story.
Separate dependent job
Check whether the LLM job lists the Terraform gate under needs. GitHub’s default dependency behavior is that a failed or skipped prerequisite causes dependent jobs to be skipped, unless a condition changes that behavior. As GitHub Docs puts it: “If a job fails or is skipped, all jobs that need it are skipped unless the jobs use a conditional expression that causes the job to continue.” See Using jobs in a workflow.
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A dependency alone is not the entire explanation: inspect the job-level if: too. A job condition can explicitly allow execution after a failure or cancellation, and conditions containing status-check functions change how the default success check applies. GitHub documents these functions in its workflow expression reference.
Reusable or independently triggered workflow
Find how the workflow containing the LLM invocation was started. A reusable workflow called by a job, a dispatch or webhook, and another event-triggered workflow can have different sequencing from a job in the Terraform workflow. Trace the trigger and its inputs rather than assuming one workflow’s job dependency controls another workflow.
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Use a success requirement for a gated LLM job
If the LLM must not run unless Terraform passes, make that requirement explicit at the job that invokes it. For a separate job, declare the dependency and check its result:
llm_review:
needs: terraform_gate
if: ${{ needs.terraform_gate.result == 'success' }}
runs-on: ubuntu-latest
steps:
- name: Run LLM review
run: ./invoke-llm-review.shReplace terraform_gate with the actual job ID and make sure that job’s success represents the intended gate outcome. Review the complete condition for any other clause that could alter the guard. If the LLM call is a step in the same job, condition it on the Terraform step’s success or on a validated output representing the gate decision.
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Avoid using always() as the condition for a costly or consequential LLM call that must be blocked by a failed gate. GitHub documents that always() remains true even when a workflow is canceled. The recommended !cancelled() pattern can suit work that should continue unless canceled, but it does not mean the gate succeeded. Use a successful-gate condition for the LLM invocation itself.
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- Open the workflow revision used by the run. Confirm the YAML at that revision, not just the current version in the repository.
- Record the gate’s actual result. Inspect the Terraform command’s exit code and step outcome, along with any policy or approval result. If
continue-on-erroris present, distinguish the step’s outcome from the job’s overall result. - Inspect the LLM step or job guard. For a same-job step, examine its
if:and any referenced output. For a separate job, checkneeds,needs.<job>.result, and the full job-level condition. - Read the condition evaluation logs. GitHub’s workflow troubleshooting documentation explains how to inspect evaluated conditions, their expanded expressions, and why a job ran or was skipped.
- Trace separate triggers. If another workflow made the LLM call, identify the event or dispatch that started it and whether that trigger carried a gate result.
These checks distinguish a missing dependency from an intentional continuation condition, a suppressed step failure, or an independent trigger. Do not attribute the run to always(), continue-on-error, or a missing needs declaration unless the YAML and run logs show that mechanism.
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Keep Terraform orchestration separate from the LLM decision
Terraform’s automation sequence should establish whether a change passed its configured checks before any downstream task that requires that result begins. HashiCorp’s guides to automating Terraform with GitHub Actions and running Terraform in automation provide context for orchestrating those stages. The LLM job should consume a clear gate result; it should not infer approval from a job merely reaching a later step.
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