AI is advancing support but leaving agents behind

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AI agents now handle the majority of routine support tickets in many organizations — fast, accurate, and on-brand, often without any human involvement.

But while automation is thriving, the human side of support is under strain.

Agents are left to tackle the toughest, most complex, and emotionally charged issues — the ones that demand empathy, judgment, and deep product knowledge. And they’re doing that work in systems that weren’t built for this level of complexity: fragmented tools, manual handoffs, constant context-switching.

Support work has evolved. The agent experience hasn’t.

Why agents are burning out faster in the AI era

As AI handles more repetitive tickets, agents are increasingly responsible for the edge cases — the nuanced, high-stakes conversations where there’s no script to follow.

This shift requires sharper critical thinking, faster onboarding, and higher emotional resilience. But most support environments still run on outdated workflows that assume the work is simple.

Agents spend precious time hunting for answers across tabs, recreating context from past tickets, and crafting responses from scratch — all before they can even begin solving the problem.

And the pressure is real. In the past year alone, 77% of agents say their workload has grown — not just in volume, but in complexity. After rolling out AI agents at Ramp, one support leader put it bluntly:

“The remaining work was all escalations. Our agents were exhausted — and our tools weren’t helping.”

New hires feel it the most. Without built-in guidance, onboarding slows to a crawl. Agents are left piecing things together, guessing at the right tone, the right path, the right response.

The result? Longer resolution times, higher stress, and a growing sense that the job is getting harder — without the systems to support it.

What this means for your business

The agent experience shapes the customer experience. When tools slow agents down or leave them guessing, it shows — in delayed responses, inconsistent tone, and critical context gaps.

These moments may seem small in isolation. But together, they add up. And they have real consequences: more than half of customers say a single bad experience is enough to make them switch to a competitor.

Burnout isn’t just a workplace issue — it’s a performance issue. It drives up attrition, slows onboarding, and erodes service quality. It also introduces risk: the kind that drags down CSAT, impacts retention, and limits your ability to scale.

Support has long been treated as a cost center. But when service quality defines customer loyalty, it’s clear: support isn’t peripheral. It’s pivotal.

Why most agent assist tools fall short

To improve the agent experience, many teams have reached for quick fixes — pre-written macros, floating toolbars, next-best-action prompts. These tools promise support, but most were built for a simpler era of customer service — one where speed and repetition mattered more than nuance and judgment.

That mismatch shows. Instead of helping, these tools often become just another layer of clutter. They surface generic suggestions without context, force agents to toggle between tabs, and rarely adapt based on what’s actually working.

As one CX leader at DailyPay shared:

“We tried a few off-the-shelf AI tools, but they just became another tab. Nothing felt like it was actually helping agents in real time.”

The future of agent work is hybrid — and connected

AI agents have redefined what’s possible in support. They resolve routine inquiries at scale and unlock meaningful efficiency. But the goal has never been to replace human agents — it’s to empower them.

Because when a conversation turns complex, personal, or high stakes, your agents step in. And they need more than a patchwork of tools to succeed.

They need systems that lift the burdens that slow them down — from typing greetings and searching knowledge bases to summarizing cases and logging notes. They need support that’s fast, context-aware, and always improving.

That’s where an AI copilot comes in. It supports agents in real time — drafting replies, translating language, surfacing relevant knowledge, and summarizing interactions — all while learning from every ticket. It bridges the gap between automation and intuition, bringing consistency and clarity to every handoff.

In a truly hybrid support model — where AI agents handle what they can and humans focus on what matters most — a copilot keeps everything in sync.

With the right mix of automation, human expertise, and real-time guidance, support becomes more than reactive. It becomes orchestrated — and ultimately, more human.