A Reddit thread on r/CustomerSuccess stopped me cold last week. A support agent at a mid-sized SaaS company had been keeping a tally on a sticky note next to her monitor, and in five business days she’d answered the same question — “how do I export my CSV?” — forty-seven times. By Friday she was reading job listings between tickets.

The replies were grim. One person said they’d quit support entirely after 18 months because the work stopped feeling like helping people and started feeling like running a script. Another posted a screenshot of a macro library with 312 templates, almost all variations on the same dozen questions.

So this is the part of customer support nobody puts on the careers page.

The repetition isn’t a bug

Most support volume is not unique. It’s the same handful of issues in slightly different costumes. Find my data, fix this button, cancel my plan. Studies from Klaus and Zendesk over the past few years have put the figure somewhere north of 70%, which means the bulk of an agent’s day is spent doing something they’ve already done hundreds of times before.

But the cognitive cost of repetition isn’t the typing. Macros and saved replies handle the typing. The cost is the constant context switching: read the ticket, classify it, hunt down the right macro, customize placeholder fields, paste in a knowledge base link, hit send, repeat. That loop is what eats people alive.

Where the vendors keep aiming wrong

Every customer support AI tool I’ve looked at over the last year wants to live somewhere other than where the agent actually works. Zendesk has Resolve. Intercom rolled out Fin. Salesforce calls theirs Agentforce. They all promise to deflect tickets before a human sees them.

Some of it works. But customers learn fast which company’s chatbot is a wall to bypass and which one actually answers their question, and the bigger problem is what happens when the bot fails: the agent picks up a transcript of the customer fighting the chatbot, writes a reply from scratch, and often apologizes on behalf of the bot that just wasted everyone’s time.

So that’s not a productivity gain. It’s a tax with extra steps.

A pattern

The closer the AI tool sits to the agent’s actual workspace, the less wasted motion. Anything further away just turns the rep into a courier shuttling text between tabs.

Why a browser agent gets at this differently

Dassi lives in your browser side panel. So it can see the ticket open in Zendesk, the knowledge base tab you have open in another window, and the Slack thread where engineering already debugged this exact issue last Tuesday. And it doesn’t need an API integration with your help desk, because the page is already on the screen, signed in as you. (Cloud agents can’t do this since they spin up their own browser somewhere else, with none of your context.)

For repetitive tickets the workflow becomes: agent reads the ticket, asks dassi to draft a reply pulling from the relevant knowledge base article and the customer’s order history, scans the draft, edits the tone, sends. Six steps shrinks to two. Agents stay in the loop on quality and voice, which is the part that matters for customer experience, while skipping the mechanical scaffolding around it.

An example from earlier this week. An agent at a fintech startup told me she handled a refund question by asking dassi to pull the customer’s last three transactions from the admin panel, check whether the payment had cleared, and summarize the timeline. Two minutes. No copy-paste, no tab roulette. The reply she sent was the same kind she’d been sending all year. But the friction in front of it was gone.

If you run a support team and want to try this on a real ticket queue, the Chrome extension is here. Agents log in with their existing ChatGPT or Claude subscription. No procurement cycle. No integration project.

A small thing about burnout

A friend who runs support at a Series B startup told me her attrition runs roughly 40% annually, which is treated as normal for the industry and would be considered absolutely insane in basically any other line of work. When she dug into exit interviews, the consistent theme wasn’t pay or career growth. It was the feeling of being a human macro.

Tools that take macro work off agents shift what the job actually is. So your most experienced support people get to focus on the weird 20% of tickets where judgment matters and the agent isn’t just fishing for the right canned response. The other 80% becomes review-and-send.

That doesn’t sound revolutionary on a slide deck. It probably won’t make a magazine cover. But the support agent who answered the CSV export question 47 times last week might still have her job in six months, and that’s worth more to me than any chatbot deflection metric I’ve seen this damn quarter.