AI AGENTS
Shell-Gated Bump with Benchmark Regression Guard
Beyond passing tests, the agent runs a benchmark in the sandboxed shell, compares it to the baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled upgrade scan
- ActionRun tests and benchmark for the bump in sandboxed shellShell
- LogicGate: tests pass and benchmark delta under threshold
- ActionOpen GitLab MR with before/after benchmark tableGitLab
- OutputReturn MR link with performance comparisonGitLab
What it does
This agent gates dependency upgrades on two signals at once: correctness and speed. It runs the test suite and a benchmark in a sandboxed shell, then opens a GitLab MR only when tests pass and performance stays within an acceptable delta of the recorded baseline.
When to use it
Use it for performance-sensitive services where a silently slower dependency is as dangerous as a broken one. The benchmark guard catches regressions that green tests miss.
How it works
- 1A schedule launches the run.
- 2The agent pins one package upgrade and, in a sandboxed shell, runs both the test suite and the benchmark script, capturing timing numbers.
- 3A logic gate compares results: tests must pass and the benchmark delta must stay under the configured threshold.
- 4If either check fails, the run aborts with a logged reason and no MR.
- 5On a clean pass, the agent opens a GitLab MR embedding the before/after benchmark table.
- 6The MR link is returned as the final output.
Set it up
What you configure once, before turning it on.
- 1Connect ShellRun sandboxed commands inside the workspace.
- 2Connect GitLabRepos, MRs, pipelines, registry.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
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Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.

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