Hey folks, I've been deep in the weeds of my IDE's AI-powered linting and refactoring suggestions lately, and it got me thinking about our support platform's new AI-assist feature. It feels conceptually similar—an automated system proposing a solution based on pattern recognition. But when a linter rule is wrong, I can disable it locally or tweak the config. When the support AI gives a flat-out wrong suggestion on a ticket... the stakes feel way higher.
We've all seen it, right? The AI jumps in with a canned response that either misdiagnoses the core issue or, worse, suggests a "solution" that could break something else. My main concern is the friction this creates. If an agent has to *undo* customer expectations set by the AI, or worse, follow a bad suggestion, it wastes more time than if the AI had just stayed quiet.
So, what's the actual, tactical best practice here? I'm thinking we need a protocol, almost like a code review for AI suggestions. I'd love to hear what's working in your teams.
* **Is there a way to "downvote" or flag a wrong suggestion directly in the ticket interface?** Not just overriding it, but feeding that signal *immediately* back to the model's training loop.
* **What about agent shortcuts?** Something akin to a VS Code keybinding that quickly dismisses the AI's comment and adds a standard "Please disregard the automated suggestion, let's look at this manually" boilerplate?
* **How granular is your deflection-rate data?** Can you isolate deflection failures caused by "AI provided incorrect steps" versus "customer needed human touch"? The metrics need to reflect operational hiccups, not just broad outcomes.
For example, in our internal setup, I've been pushing for something like a tagging system in the agent workspace:
```json
{
"ai_suggestion_quality": "incorrect",
"failure_mode": "misdiagnosed_root_cause",
"suggestion_id": "AI-SUP-5432",
"override_action": "used_custom_template_12"
}
```
This structured data would be gold for the platform devs to improve things. Are any of you logging this level of detail, and if so, how? I'm really curious about the workflow integrations that turn these inevitable wrong turns into useful training data, without burying the agent in extra clicks.
editor is my home
SRE at a mid-sized fintech, managing the stack that watches the stack. We run Grafana, Prometheus, and OpenTelemetry-collected logs in Loki.
The core problem is treating AI suggestions as authority instead of a draft. You need a protocol that treats them like a junior engineer's first pass.
**Feedback Loop Speed**: The critical metric is how long it takes for a flagged bad suggestion to stop appearing. In our setup, a "mark as incorrect" button feeds a separate review queue, but the model only updates weekly. That's too slow for a live ticket flow.
**Override Friction**: The extra clicks required to dismiss and then manually act. Our current tool adds two steps: "ignore suggestion" and then "type reason". That's often more work than just solving the ticket from scratch, which defeats the point.
**Training Signal Isolation**: You need to separate "this is wrong for this ticket" from "this pattern is generally wrong". We tag suggestions with the ticket's actual category (e.g., `billing_inquiry`) so the feedback is scoped. Without that, a valid suggestion for password resets could get penalized because it was wrongly applied to a billing issue.
**Agent Context Cost**: The hidden cost is the time an agent spends understanding why the AI was wrong, to avoid repeating it to the customer. We measured this: a wrong suggestion adds ~90 seconds of mental context-switching per ticket, on average.
My pick is to implement a one-click "override & flag" that immediately hides the suggestion and tags it for review, but the agent continues in their normal flow. For a clean recommendation, tell us: 1) the average time your agents spend on a ticket now, and 2) whether your AI suggests *actions* (like "refund $X") or just *knowledge* (like "link to policy Y").
More dashboards != better ops