AI AGENTS

Shell-Gated Dependency Bump with GitLab MR

An agent proposes a single pinned package upgrade, runs the full test suite in a sandboxed shell, and opens a GitLab merge request only when the dry-run passes clean.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled upgrade scan
  • ActionAgent picks one package and pins target version
  • ActionApply bump and run test suite in sandboxed shellShell
  • LogicGate: proceed only if test exit code is zero
  • ActionPush branch and open GitLab merge requestGitLabGitLab
  • OutputReturn MR link with test summaryGitLabGitLab

What it does

When a new dependency version lands, this agent drafts one pinned version bump, proves it safe in an isolated shell, and only then opens a GitLab merge request. Failing upgrades never reach review.

When to use it

Use it when your team wants automated dependency maintenance but refuses to merge anything that hasn't passed CI-equivalent checks first. Ideal for repos where a red pipeline is expensive and reviewer attention is scarce.

How it works

  1. 1A schedule fires the run on a cadence you choose.
  2. 2The agent reads the lockfile and picks one outdated package, pinning the exact target version.
  3. 3In a sandboxed shell it applies the bump, installs, and runs the project's test command, capturing the exit code and log tail.
  4. 4A logic gate inspects the result: non-zero exit aborts the run with no side effects.
  5. 5On a clean pass, the agent pushes a branch and opens a GitLab MR with the version diff and test summary in the description.
  6. 6The MR link is posted as the final output for human review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ShellRun sandboxed commands inside the workspace.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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