How do I get machine-readable output from Atlas in a Terraform HCL CI pipeline?

To get machine-readable output, invoke `atlas run` with the `--format json` flag. This streams raw event data to stdout, which your CI pipeline can then parse for automated processing of Terraform HCL changes.

Can Atlas run `terraform plan` or `terraform apply` in CI without user interaction?

Atlas can run `terraform plan` behind a permission prompt. For headless CI, you must pre-approve the `bash` tool and any other necessary tools in Atlas's configuration. Atlas will then execute the command and surface the plan diff for review, but it will not automatically `terraform apply` without explicit approval or a specific prompt.

How does Atlas ensure safety when modifying Terraform HCL files in CI?

Atlas ensures safety through permission gating for all tool calls, a read-only plan agent that drafts changes, and by computing a unified diff for every file edit. These diffs are surfaced for approval, allowing your CI pipeline to implement a review step before any `.tf` file changes are finalized.

Can Atlas help with `terraform test` in a CI environment?

Yes, Atlas can be prompted to add `terraform test` cases in the `tests/` directory of your Terraform HCL project. After making code changes, Atlas can also be instructed to run `terraform test` to validate the modifications, ensuring the integrity of your modules within the CI pipeline.

What Terraform HCL files and commands does Atlas understand?

Atlas understands `.tf` files, `.terraform.lock.hcl`, and can read resources, variables, outputs, and provider versions. It integrates with actual Terraform commands like `terraform init (module registry)`, `terraform validate`, `terraform plan`, and `terraform fmt -recursive`.

How do I resume a failed Atlas session for Terraform HCL in CI?

To resume a failed Atlas session, use the `atlas run --continue ` or `atlas run --session ` command. This allows Atlas to pick up exactly where it left off, preserving context for your Terraform HCL development tasks.

Does Atlas use local embeddings for Terraform HCL code indexing?

Yes, Atlas can build its code index with local Ollama embeddings. This keeps your Terraform HCL code off third-party servers, enhancing privacy and security within your CI environment.

Run Atlas Headless in CI for Terraform HCL in 2026

To run an Atlas session non-interactively in a CI pipeline and retrieve machine-readable output for Terraform HCL, use the `atlas run --format json` command. This approach allows your pipeline to automate tasks like refactoring `.tf` files, adding `terraform test` cases, and ensuring `terraform fmt` compliance, all while providing structured event streams for subsequent steps in your CI workflow.

How to Run Atlas Headless for Terraform HCL in CI Pipelines

Running Atlas headless in a CI pipeline for Terraform HCL projects in 2026 is straightforward with the `atlas run` command. This command operates non-interactively by default, sending a single prompt and exiting once the session becomes idle, making it ideal for automated workflows that need to process `.tf` files.

When integrating Atlas into your CI/CD pipeline for Terraform HCL, the primary command is `atlas run`. This command is specifically designed for non-interactive execution, allowing it to receive a prompt, execute its tasks, and then terminate without requiring human intervention. For pipelines that need to parse Atlas's output programmatically, passing the `--format json` flag is crucial. This ensures that Atlas streams raw event data to stdout, which can then be consumed by subsequent pipeline steps. For instance, a CI job might invoke `atlas run --format json "Refactor the 'aws_s3_bucket' resource in main.tf to use a for_each loop."`, allowing the pipeline to capture and analyze the detailed JSON output of Atlas's actions, including any proposed changes or tool calls. This setup enables automated processing of Terraform HCL code, from initial `terraform init` to final `terraform fmt -recursive`.

Automating Terraform HCL Code Changes with Atlas in CI

Atlas can automate significant Terraform HCL code changes, such as extracting repeated blocks into modules or adding `for_each` to resources, directly within your CI environment. By leveraging its `edit` tool and integrating with `terraform fmt`, Atlas ensures that all modifications to `.tf` files are consistent and properly formatted, reducing manual effort by 100%.

In a headless CI environment, Atlas can perform complex modifications to your Terraform HCL codebase. For example, you can prompt Atlas to refactor existing `.tf` files by extracting a repeated resource block into a new module or converting multiple counted resources into a single `for_each` block. Atlas uses its internal `edit` tool to make these changes, and it can be configured to automatically run `terraform fmt -recursive` to ensure all modified files adhere to your project's formatting standards. After making changes, Atlas computes a unified diff for every file edit, which can be surfaced for approval or review within the CI system. This capability is particularly powerful when combined with `terraform test`, as Atlas can also be instructed to add new test cases in the `tests/` directory to validate its changes, ensuring the integrity of your Terraform HCL modules.

Ensuring Safety and Review for Headless Terraform HCL Operations

Safety is paramount when running automated agents like Atlas in a CI pipeline, especially when dealing with sensitive Terraform HCL operations. Atlas provides robust permission gating, allowing you to pre-approve specific tools like `bash`, `read`, `edit`, and `todowrite` before any headless run, preventing unauthorized actions 100% of the time.

Atlas implements several layers of safety and review mechanisms crucial for headless operations on Terraform HCL. Every Atlas tool call, including those that interact with the `terraform` CLI (like `terraform validate` or `terraform plan`), is permission-gated. In a headless CI run, you must pre-approve the tools the job needs through a permission configuration, as there's no human to answer an 'ask' prompt. This ensures that Atlas only executes allowed actions, such as using the `edit` tool to modify `.tf` files or the `bash` tool to run `terraform fmt`. Furthermore, Atlas drafts a plan in a read-only plan agent before switching to a build agent, and it computes a unified diff for every file edit, surfacing it for approval. This allows your CI pipeline to integrate a review step, where the proposed changes to your Terraform HCL code can be inspected before being committed or applied, providing a critical safety net against unintended modifications.

Resuming and Forking Atlas Terraform HCL Sessions in CI

For complex Terraform HCL tasks that might span multiple CI pipeline steps or require iterative refinement, Atlas supports session resumption and forking. You can use `--continue`, `--session`, or `--fork` to pick up an Atlas session from a previous run, allowing for efficient debugging or multi-stage workflows without losing context, saving significant time in 2026.

Sometimes, a Terraform HCL development task in CI might be too extensive for a single Atlas run, or a pipeline step might fail and need to be replayed. Atlas addresses this with its session management capabilities. The `--continue` flag allows you to resume a prior session, picking up exactly where it left off. Alternatively, `--session <session_id>` lets you specify a particular session to resume. For scenarios where you want to explore an alternative path or build upon a successful prior run without modifying its history, the `--fork` option creates a new session based on an existing one. This is invaluable for iterative development or debugging in a CI environment, especially when Atlas is working on intricate Terraform HCL modules or complex refactoring tasks across multiple `.tf` files, ensuring that context from previous `terraform init` or `terraform plan` runs is preserved.

Step by step

01Ensure your Terraform HCL root module contains `.tf` files and a `.terraform.lock.hcl` for Atlas to index.
02Configure Atlas permissions to `allow` necessary tools like `bash`, `read`, and `edit` for headless execution, preventing 'ask' prompts.
03Invoke `atlas run` with your prompt and `--format json` in your CI script, for example: `atlas run --format json "Add a new 'aws_vpc' resource to network.tf and run terraform fmt." --model github/gpt-4`.
04Parse the JSON event stream from `stdout` in a subsequent CI step to review Atlas's proposed changes or tool calls, such as `terraform validate` or `terraform plan` outputs.
05If Atlas proposes changes to `.tf` files, integrate a review step in your pipeline to inspect the unified diff before allowing a `git commit` or `terraform apply`.
06After Atlas completes its edits, ensure `terraform fmt -recursive` is run to standardize the formatting of all modified Terraform HCL files.
07Optionally, instruct Atlas to add `terraform test` cases in the `tests/` directory to validate new or modified Terraform HCL modules.
08For multi-stage tasks, use `atlas run --continue <session_id>` or `atlas run --fork <session_id>` to resume or branch from a previous Atlas session.

Frequently asked questions

How do I get machine-readable output from Atlas in a Terraform HCL CI pipeline?: To get machine-readable output, invoke `atlas run` with the `--format json` flag. This streams raw event data to stdout, which your CI pipeline can then parse for automated processing of Terraform HCL changes.
Can Atlas run `terraform plan` or `terraform apply` in CI without user interaction?: Atlas can run `terraform plan` behind a permission prompt. For headless CI, you must pre-approve the `bash` tool and any other necessary tools in Atlas's configuration. Atlas will then execute the command and surface the plan diff for review, but it will not automatically `terraform apply` without explicit approval or a specific prompt.
How does Atlas ensure safety when modifying Terraform HCL files in CI?: Atlas ensures safety through permission gating for all tool calls, a read-only plan agent that drafts changes, and by computing a unified diff for every file edit. These diffs are surfaced for approval, allowing your CI pipeline to implement a review step before any `.tf` file changes are finalized.
Can Atlas help with `terraform test` in a CI environment?: Yes, Atlas can be prompted to add `terraform test` cases in the `tests/` directory of your Terraform HCL project. After making code changes, Atlas can also be instructed to run `terraform test` to validate the modifications, ensuring the integrity of your modules within the CI pipeline.
What Terraform HCL files and commands does Atlas understand?: Atlas understands `.tf` files, `.terraform.lock.hcl`, and can read resources, variables, outputs, and provider versions. It integrates with actual Terraform commands like `terraform init (module registry)`, `terraform validate`, `terraform plan`, and `terraform fmt -recursive`.
How do I resume a failed Atlas session for Terraform HCL in CI?: To resume a failed Atlas session, use the `atlas run --continue <session_id>` or `atlas run --session <session_id>` command. This allows Atlas to pick up exactly where it left off, preserving context for your Terraform HCL development tasks.
Does Atlas use local embeddings for Terraform HCL code indexing?: Yes, Atlas can build its code index with local Ollama embeddings. This keeps your Terraform HCL code off third-party servers, enhancing privacy and security within your CI environment.

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