Has anyone benchmarked the new AI-assist against a human-only baseline?

Your skepticism is correct. Vendor benchmarks are often optimistic internal tests.

We ran a 90-day pilot with an AI-assist tool for our platform SRE team. Key finding: average handling time dropped for simple, documented issues like the GitHub Actions secret reset you mentioned. First-contact resolution didn't budge for complex, novel pipeline failures. The AI frequently suggested outdated Helm chart configurations for our K8s setup, which added verification overhead.

The real cost is in the noise. You need a senior engineer to constantly curate its knowledge base and review its suggestions, or it becomes a liability. Measure that operational overhead in your comparison.

Yep, the operational overhead is the hidden tax. It's not just curating the knowledge base - it's the constant second-guessing that bleeds time. I've seen agents start to blindly trust an AI's canned response after a few "hits," leading to a bigger miss later on when it confidently recommends the wrong API endpoint. You benchmark the time it *saves*, but you also have to measure the time it *creates* through damage control.

Good point about IDE suggestions. I've also seen some that were way off.

I wonder, does anyone track how often agents ignore the AI suggestions completely? That could be another data point.

Also, are there any good ways to measure that "second guessing" time the last comment mentioned? Seems tricky.

That point about the AI suggesting outdated Helm chart configs hits home. We saw something similar with deployment troubleshooting, where the AI would pull from old tutorials referencing deprecated Prometheus scrape configurations.

We actually tried to quantify that verification overhead you mentioned. We added a custom tag in our ticketing system for "AI-suggested-solution-verified," and tracked the time from AI suggestion to agent's first manual action. For those outdated configs, the median verification time was nearly 3 minutes, which often negated the time saved on the initial reply.

So the benchmark needs a third column: time saved *if correct*, time wasted *if outdated*, and the probability of each.

Yeah, the IDE suggestions are tricky. I was trying to use a cloud provider's AI tool to write a Terraform security group rule and it suggested opening port 22 to 0.0.0.0/0 for an internal app. That would have been a bad night.

Have you seen any specific problems with CI/CD suggestions, like for GitHub Actions or something? I'm worried about those too.

Great question. The thread's already moving in a really useful direction with the focus on verification overhead and the risk of outdated configs, which I think gets to the heart of your skepticism about vendor claims.

One practical approach I've seen is a split-team pilot, where one group of agents uses the AI-assist and a control group doesn't, tracking those exact metrics you listed over a set period for the same ticket types. The key is also logging every instance an agent overrides or ignores an AI suggestion, and categorizing why. That can give you a "noise ratio" to weigh against any time savings.

For your CI/CD context, pay close attention to suggestions involving specific versions of tools or services. An AI might save time on a general "pipeline stuck" question but then suggest a `docker-compose` fix for a pure Kubernetes deployment, costing more time in correction.

That "noise ratio" is a clever metric, and logging overrides gets to the practical reality. It reminds me of a pilot where we tracked the override reason "suggestion irrelevant to context." We found a surprisingly high number of AI suggestions that, while perhaps factually correct, were for a different part of the stack than the ticket was about, like offering database tuning when the issue was about frontend asset loading.

Your point about specific versions is critical. For CI/CD, a suggestion for a `git` command flag that changed between versions can break a script just as easily as a wrong `docker-compose` suggestion. The split-team pilot works best if you can also track the time lost to those context mismatches.

Your starting point with those three metrics is solid, but from an integration standpoint, you need to instrument for the data flow between the AI and the agent. The "average handling time" metric only tells half the story if you aren't also measuring the latency and correctness of the AI's data retrieval from your internal knowledge bases and API docs.

For CI/CD specifically, like your Docker build example, the biggest risk I've mapped is in action sequencing. An AI might correctly suggest a `docker system prune` command but fail to contextualize that it will disrupt other concurrent builds on your shared runner, effectively trading a quick ticket close for a pipeline cascade. That's a data mapping failure between the isolated ticket context and the wider platform state.

I'd modify your benchmark to treat the AI as a separate, potentially flaky API. Log its response time, its "hit rate" for useful suggestions, and its "error rate" for suggestions that are outdated, insecure, or contextually wrong. Then you can calculate a true net efficiency score.

Treating it like a flaky API is exactly how we've modeled it. That "error rate" needs a severity dimension, though. A slow response is just a latency hit, but a wrong suggestion for a destructive command like `docker system prune` on a shared runner is a high-severity incident. Your instrumentation needs to capture that difference, or your efficiency score will be dangerously optimistic.

The latency and correctness point about data retrieval is spot-on. The most concrete benchmark I've seen measured "time-to-correct-answer" by splitting it into AI latency *plus* human verification time. For some simple FAQs, the AI's latency was negligible and verification was quick, leading to a net gain. But for complex CI/CD tickets like a Docker build failure, the AI's latency to comb through your specific build logs could be 10-20 seconds, and then the verification overhead could be another minute or two if the agent is double-checking commands. That can sometimes put the total "assisted" time higher than an experienced agent just diagnosing it themselves.

So the baseline comparison isn't just human vs. human+AI, it's really about the *quality* of the data the AI has to work with. If your internal wiki on pipeline errors is a mess, the AI will have high latency and low correctness, and your benchmark will likely show a loss. I'd suggest running your pilot on a subset of tickets where your documentation is known to be solid first, to see the best-case scenario.

Your Terraform example is a perfect high-severity case. That's a security incident waiting to happen.

For GitHub Actions, the biggest issue I've benchmarked is around composite actions and the `uses:` key. The AI will often suggest an action with a major version pin (like `uses: actions/checkout@v3`) when v4 has been out for a year with critical security fixes. The suggestion isn't "wrong," but it introduces technical debt and vulnerability the moment it's merged.

The verification overhead for that is real - you have to check the action's repo for the latest stable tag, which adds another minute.

Your metrics are good, but you need to baseline them in your vendor contract. Vendors love to quote internal studies on "time saved." Get them to agree to a pilot period where you define the control group and track the "noise ratio" others mentioned. Make the success metrics part of the SLA.

The real cost isn't just slower tickets, it's the liability. If an AI suggests an insecure GitHub Action pin like user846 said, that's a security finding. Most SaaS agreements won't cover that. Negotiate a clause that the vendor is responsible for updating their training data quarterly, and you get an out if data freshness lags your core tool versions.

That median verification time of 3 minutes is the hidden tax everyone misses. It lines up with what we measured for outdated Docker base image tags in our pipelines - the AI would pull suggestions from old blog posts using `node:14` when `node:18` was LTS.

Your third column framework is smart, but I'd add that the probability isn't static. It gets worse for niche or rapidly evolving tools. The AI's training data for something like Argo CD or a specific GitHub Action is almost always a version behind, so the probability of an outdated config suggestion spikes right after a major release.

Flaky API logging is a good start, but you're still just measuring operational waste. The real cost is the license fee you pay for that flakiness.

That "net efficiency score" needs a dollar sign. If the AI's latency adds 10 seconds and verification adds 3 minutes per ticket, you're paying for an AI seat *and* burning senior agent time. At scale, that's negative ROI dressed up as a tech problem.

And good luck getting a vendor to accept liability for that "pipeline cascade" from a bad suggestion. Their SLA will cover uptime, not your cleanup costs.