TIL: You can trigger evals from Freeplay's API, not just the UI

You're right about the immediate feedback, but posting the results to Slack after every main branch merge is going to create so much noise you'll start ignoring it within a week. The channel will become a graveyard of reports.

If you're committed to the Slack path, your script needs to enforce a ruthless binary: it posts nothing on a pass, and only an alert with a direct link to the failed run on a genuine regression. Anything more and you're training your team to mute the channel.

That hybrid polling approach is the pragmatic middle ground. It's the same pattern we use for async jobs from our middleware platform.

The key is structuring your poll so it respects the external system's queue. A linear backoff that's too aggressive just adds load, but a naive long timeout defeats the CI/CD speed requirement.

We set the max wait based on the typical eval runtime plus a buffer, and if we hit that limit, we log it as a "quality gate incomplete" warning rather than a hard failure. The pipeline proceeds, but the issue is tracked.

> We set the max wait based on the typical eval runtime plus a buffer, and if we hit that limit, we log it as a "quality gate incomplete" warning

This is a solid strategy. We do something similar, but we also tag those "incomplete" warnings with a specific high-severity flag in our monitoring dashboard. It's not a pipeline failure, but it *does* automatically create a ticket for the platform team to investigate the delay cause - was it a Freeplay queue issue, or did our eval suite balloon in size without us noticing?

Makes sure the warning doesn't just get lost in a log stream.

Creating a ticket on an incomplete warning is the right operational discipline. The risk is that your platform team ends up with a ticket queue full of transient noise if the external service has any instability.

You need to pair that automatic ticket creation with an equally automatic cleanup process. If the next eval run for the same prompt version completes successfully within, say, an hour, the system should automatically resolve the ticket without human intervention. Otherwise you're just building alert fatigue into a different team.

That follow-up automation is what separates a nagging system from a useful one.

That automated Slack feedback loop is a great way to keep the team in sync. Just a heads-up from our own experience, you might want to start with a channel that's just for the bot or a very small group. When everyone gets pinged for every merge, it can quickly lead to notification overload and important alerts get missed.

I'd be interested to hear how you handle the async result fetching in your GitHub Action. Do you poll for completion, or do you just fire and forget and check the results later?

That's a solid practice, starting with a dedicated bot channel. We did the same, and it became the single source of truth for the team leads to glance at.

On the async polling in GitHub Actions, we use a composite action with a simple exponential backoff. It polls for the run status, and we fail the pipeline step only if the final result is a genuine failure. If the poll times out after our max wait, we emit that "quality gate incomplete" warning others mentioned and let the pipeline proceed. It's a balance between getting a result and not holding up a deploy.

Integrating eval triggers into CI/CD is the logical next step for maturity, but you're right to focus on the *results* automation. The Slack channel approach has operational cost. Have you quantified the notification load versus actual intervention rate?

In our setup, we log every eval run to a time-series database (like Datadog or Grafana) and only surface alerts when a metric breaches its historical control limits. This separates the monitoring signal from the deployment mechanism. The API call is just the ingestion point.

The more subtle cost is the compute time for the eval suite itself. If you're running this on every main branch merge, you need to track the cumulative Freeplay spend as part of your pipeline's total cost of ownership. A suite that takes 10 minutes to run can become a significant line item at scale.

Great use case. The API integration was our turning point too.

We added a second trigger based on our monitoring. If we see a spike in user-reported "weird answers" from our LLM in production, an automation kicks off a targeted eval suite to see if it's a prompt drift issue vs a model problem.

Just watch your Freeplay costs. Those automated evals add up fast if you're running them on every merge. We set up a monthly budget alert after getting a surprise bill.

That's a brilliant use of the API for reactive monitoring, turning user feedback into a diagnostic signal. We took a similar path but paired it with a tagging system.

We tag each triggered eval with the reason it was launched - "user-report-spike", "scheduled-merge", "prompt-refactor". After a few months, you can filter your Freeplay bill by tag to see exactly where the costs are coming from. Turns out our "ad-hoc-investigation" tag was the real budget killer, not the CI/CD merges.

The surprise bill is a rite of passage, isn't it? A budget alert is an absolute must.

Glad you found the API. The CI/CD trigger is the obvious first move, but it's the *other* automation where things get interesting.

We've tied ours to incident tickets. If a support ticket gets tagged with a specific label related to the LLM output, it automatically triggers a targeted eval suite. This helps us immediately rule out prompt regressions and direct engineering to the actual problem, be it context, model, or data.

Just keep an eye on how many evals you're stacking. That Slack channel can become a firehose of 'everything is fine' messages, and then you're right back to the manual check problem, just with more noise.

Great find on the API! That CI/CD trigger is exactly how we started. We also added a scheduled nightly run for our core prompts, just as a baseline check against model drift. It's cheap peace of mind.

One heads-up - make sure you're handling the async nature properly. The API call returns a run ID immediately, but the results come later. We built a small retry loop to fetch them, otherwise your Slack channel might just get "eval started" notifications.

Have you considered tagging those automated runs? It makes slicing your Freeplay bill later a lot easier to figure out what's CI cost vs. ad-hoc testing.

Nightly baseline runs are smart, but have you validated they actually catch drift? We tried that and the signal was too noisy until we set a proper SLO on the metrics. Just running them isn't enough; you need a clear threshold for what constitutes a regression.

> handling the async nature properly

Our retry loop includes a timeout and a circuit breaker. If Freeplay is having an issue, we don't want our CI/CD pipeline stuck in a retry hell. It fails the quality gate and moves on.

We used that same trigger to block merges. If the eval run fails, the GitHub Action step fails and the PR can't be merged.

Be careful with timeout values. Our suite can take 8+ minutes during peak load, so we had to extend the default job timeout in the workflow YAML.

Automating the trigger is the easy part, the hard part is defining what constitutes a "failure" that should block a merge. A suite that flags every minor fluctuation as a failure will grind your team to a halt, or worse, train them to ignore the results. What's your actual threshold for a performance degradation?

Yes, the `testSuiteId` is in the URL when you're viewing the suite in the UI. It's that long alphanumeric string after `test-suites/`.

On the delay, the API call to *start* the eval is near-instant, but the suite execution time depends entirely on its size and complexity. For a small suite, it's a few seconds. For a large one with hundreds of test cases, it can be several minutes, as others have mentioned. So your merge process won't be waiting for the kickoff, but it will need to wait for completion if you're gating on the results. Build that polling timeout accordingly.