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

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(@franklin)
Trusted Member
Joined: 3 weeks ago
Posts: 47
 

The liability angle is something I hadn't considered. Does pushing for that "vendor is responsible for updating their training data" clause ever work? I've seen vendors push back, saying their model is general-purpose and it's on us to configure the guardrails for our specific versions.



   
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(@averyt)
Estimable Member
Joined: 2 weeks ago
Posts: 88
 

That "noise ratio" is the hardest part to quantify before a pilot, but it's absolutely crucial. We saw similar drops in handling time for our basic onboarding ticket templates, where the AI just fills in user details and sends a prefab email.

But that verification overhead your senior engineers do is real time and mental load. It's not just checking a suggestion, it's the context switching cost for them to pause their own deep work to play editor. Even a "quick review" adds up fast if they're doing it dozens of times a day.

The real question is whether that freed-up time from the simple tickets outweighs the new curation job you've created.


Automate all the things


   
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(@cloud_cost_auditor)
Reputable Member
Joined: 3 months ago
Posts: 173
 

Vendors love those efficiency gain blogs, don't they? The metrics you listed are the right starting point, but you need to build your own cost baseline.

The real number is time-to-*verified*-correct-answer. You have to factor in the human verification tax on every suggestion, especially for anything touching security or infrastructure. That "outdated or insecure config" worry is real - we've seen suggestions for deprecated GitHub Action versions and old Docker tags. The verification overhead for a senior agent can wipe out any time saved on the initial response.

Run your pilot, but track the license cost per ticket against your fully-loaded agent hourly rate. I've seen the math go negative fast when you account for the senior time spent curating bad suggestions.


Show me the bill


   
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(@ethanm)
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Joined: 3 weeks ago
Posts: 77
 

Totally agree on building your own baseline. The verification tax is what we missed in our trial.

We saw the math go negative when the AI gave an outdated API endpoint for a CMS webhook. The senior agent had to stop, check the docs, then correct it. The "time saved" on the initial draft was wiped out by that research pause.

How do you even start tracking that verification time accurately? Our agents just lump it into "work time."



   
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