ENGINEERING
Agent-driven Replicate release review with bench investigation
An agent investigates a candidate Replicate version: runs the bench, reads the failing cases, cross-checks the model card on Hugging Face, writes a reasoned release recommendation.
How it runs
The automated pipeline, trigger to output.
- TriggerMaintainer requests release review
- ActionRun regression bench on candidateReplicate
- ActionRead model card + changelogHugging Face
- LogicReason over trade-offs, draft recommendation
- ActionOpen PR review with promote/hold proposalGitHub
- OutputPost recommendation to Slack for approvalSlack
What it does
Runs an autonomous release reviewer instead of a fixed threshold check. When a candidate Replicate version is ready, the agent executes the bench, reads which cases regressed and why, cross-references the model card and changelog on Hugging Face, and writes a reasoned promote-or-hold recommendation with evidence, leaving the final call to a human.
When to use it
Use it when a single accuracy threshold is too blunt and you want judgment about whether a regression is acceptable for a given release. Good for nuanced trade-offs where latency improved but one category got slightly worse.
How it works
- 1A maintainer requests a review for a candidate version, starting the agent.
- 2The agent runs the regression bench against the candidate on Replicate.
- 3It inspects the failing and improved cases and reads the model card and changelog on Hugging Face.
- 4It reasons over the trade-offs and drafts a recommendation with supporting evidence.
- 5It opens a GitHub PR review comment with the writeup and a promote/hold proposal.
- 6It posts the recommendation to Slack and waits for human approval before any alias change.
Set it up
What you configure once, before turning it on.
- 1Connect ReplicateImage, video, and model inference.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Engineering workflows
Gate breaking API PRs behind downstream consumer acknowledgement
When a PR introduces a breaking contract change, comments the impact summary back on the PR, applies a blocking label.
Publish a versioned API changelog to Confluence on each release tag
On a new semver release tag, gathers the contract changes since the last release and writes a clean.
Agent reviews model-license fit and suggests compliant swaps on the PR
When a PR adds a Hugging Face model, an agent reads the model card and license, judges fit against your commercial-use policy.
Upgrade Impact Router to Module Code Owners
Maps a dependency-bump PR's affected modules to their CODEOWNERS, then DMs each owner on Slack with only the changelog slice that touches code they own.
Re-Voice IVR Prompts on Phone-Tree Config Merge
When a phone-tree config change merges in GitHub, regenerates the ElevenLabs audio for any prompt whose script changed in the diff and opens a follow-up PR adding the new audio…
Upstream Release to Notion Upgrade Brief
When a watched package publishes a new release, fetches the release notes, maps them to the internal modules that depend on it.
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.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.