Many modern businesses look to automate ticket handling, but fully removing human agents from the support flow is a bad idea unless all your requests are straightforward and repetitive.
In complex, context-rich environments, human support is still crucial for delivering the level of service customers expect.
But that doesn’t mean support can’t be more efficient.
Agent copilot tools are designed to boost your human agents’ performance — for example, with AI-generated drafts, ticket summaries, and guidance in real time.
They’re also a great way for teams to get comfortable with AI before rolling out automation that talks to customers directly.
That said, like any emerging technology, especially one that’s still evolving, there’s still room for growth. In this article, we’ll go over a practical wish list of agent copilot capabilities that support leaders would love to see to better empower their teams.
What Is an Agent Copilot?
An agent copilot is an AI-powered tool designed to boost your support reps’ productivity by automating tasks, offering smart suggestions, and providing real-time insights.
It works behind the scenes to help your team deliver faster, more accurate, and more personalized support, improving both efficiency and customer satisfaction.
What does that look like in real life? Say a customer reaches out because the lamp they bought isn’t working. Instead of digging through docs and tools, your agent can:
- Ask Copilot for the right troubleshooting steps.
- Generate a quick draft reply.
- Then have Copilot polish it to sound friendlier and better match the customer’s tone.
The human still makes the final call, but gets to a great answer much faster. That’s already pretty helpful, and tools like Zendesk Agent Copilot or Intercom Copilot can offer this experience today.
Now let’s take it one step further: in a perfect world, what would an ideal agent copilot look like? How could it bring even more value to your team?
And are there solutions out there already starting to fill those gaps?
Support Leaders Wishlist for Agent Copilot Tools
Here are the top nine features support leaders would love to see in copilot tools to better equip their teams.
Better integrations and richer learning sources
Most copilot tools focus on suggesting a draft reply and recommending the next best step toward resolving the customer’s issue. That’s useful, but the real differentiator is how the copilot learns.
Does it only pull from your help center and saved macros, like Zendesk’s suggested first reply? Or can it learn from what actually powers your team day to day: thousands of past tickets, selected Slack channels, and the most recent incidents, bug reports, and postmortems?
Macros and help center search absolutely have a place.
But when an agent is working a tier 2 ticket (or even a slightly unusual tier 1 case), being prompted to use the “Greeting” macro, because it’s technically the most common pattern across all tickets, is just distracting.
The most practical fix is AI-driven knowledge management tools that work across the company, consolidating information from multiple systems and surfacing the right context directly inside the helpdesk.
The issue here is that many of these tools are built for broad enterprise knowledge or sales use cases rather than support-specific workflows.
As a result, their helpdesk integrations are often too weak, and agents can’t insert the generated reply into the composer with a single click or reliably pull the latest internal context without leaving the ticket.
Option to select copilot sources per agent
Experienced agents already know the macro library and the help center inside out. They don’t necessarily need a copilot to help them search resources they can recall in seconds.
In fact, I’ve repeatedly heard the same feedback from senior agents on my team: copilot that defaults to macros and help articles can feel distracting and slower than just handling the ticket directly.
For new agents, it’s the opposite. That same support is genuinely valuable because they haven’t memorized the macros or built the instincts to know which article maps to which issue.
That’s why teams with mixed experience levels should be able to tailor what the copilot learns from and suggests on a per-agent basis.
The ideal copilot should adapt to the experience level by letting admins configure (and agents refine) the sources it pulls from.
Ideally, newer agents could default to the most structured sources first (macros, help center articles, internal playbooks), while experienced agents could prioritize deeper, higher-signal sources when they need help (closed tickets, relevant Slack threads, bug reports, incident notes, and product updates).
Then, when a senior agent is stuck or dealing with an edge case, the copilot becomes a real help rather than a noisy search assistant.
Voice & tone settings for internal copilot replies
Most copilot tools support both internal mode, where reps ask questions to figure out how to proceed, and customer-facing reply drafts.
Customer-facing outputs usually come with advanced controls like persona settings, tone sliders, and brand voice guidelines for responses to match your company’s communication style.
Internal replies, however, are typically generated in a default, overly formal style with little or no tone customization. That’s a problem if your brand voice and team culture are naturally warm, friendly, and conversational.
When agents want to use a portion of that internal guidance in a customer-facing reply, they either have to manually rewrite the text to match the way the team speaks, or run it through a separate “make this friendlier” feature.
Those extra steps don’t sound like much, but at scale, they add up and interrupt the flow.
Support leaders need the same level of tone control for internal copilot replies as they have for customer-facing messages.
Internal guidance should feel like it was written by a helpful teammate, not a generic corporate assistant, and it should be configurable upfront so agents don’t have to spend extra clicks fixing it.
Custom prompts for translations
Translations are another area where copilots could use some improvement.
Many languages have built-in choices that don’t exist in English, like formal vs. informal address (Sie vs. du in German), or different levels of politeness and distance.
Those nuances aren’t obvious when you’re drafting in English, which means agents can’t reliably predict what tone the copilot will default to once it translates the message.
Here’s what typically happens when supporting an international customer:
- An agent uses a translation feature to read the customer’s message in English,
- Drafts the reply in English,
- And then asks the copilot to translate it back (or the helpdesk translates it automatically on send).
The risk is that the customer receives a message that’s technically correct, but stylistically wrong: too formal, too casual, or simply not aligned with how your brand communicates.
What’s missing in many copilot tools today is the ability to customize translation behavior through a dedicated prompt or settings layer.
Admins should be able to define translation rules the same way they define brand voice for customer-facing replies, choosing their preferred tone, level of formality, and even phrases to avoid.
Teams running on Zendesk, for example, can address this gap by using a tool like Swifteq’s Agent Co-writer, which allows you to fine-tune translations by customizing the translation prompt.
Agents copilot to have better simulation mode
One of the biggest concerns with launching AI agent copilot tools is the risk that it goes off-script, hallucinating and giving incorrect instructions.
And without a reliable way to test the system before it goes live, you’re essentially crossing your fingers and hoping for positive feedback from your team after the launch.
For instance, that’s still a noticeable gap in Zendesk’s current Agent Copilot offering.
There isn’t a strong simulation mode, which makes it hard to predict real-world performance, so admins end up relying on a handful of manual spot checks, which rarely reflect the messy edge cases that happen in actual support.
What support admins need is a true “sandbox” with the ability to run copilot suggestions against historical tickets and evaluate the output before enabling it for their teams.
Being able to simulate what guidance the copilot would have offered for recently solved tickets, at scale, would be a game-changer for building confidence.
Option to rate reply and suggest improvements on the spot
Many copilot tools, including Zendesk and Intercom, let agents rate AI-generated replies. In theory, that’s a helpful feedback loop.
In practice, the feedback often goes directly to the copilot system’s product managers and never reaches the workspace admins who are responsible for configuring the copilot’s sources, guidelines, and workflows.
That gap creates confusion and trust issues. Agents assume their feedback will help improve the copilot when they flag missing knowledge, incorrect guidance, or tone issues.
But nothing changes, since admins aren’t notified, can’t review the patterns, and can’t act on what agents are reporting.
Over time, reps stop using the feedback buttons because they don’t believe they make any impact. As one Zendesk community member put it:
“While the new feedback function in Copilot auto assist is probably a good feedback solution for Zendesk itself, it creates a lot of confusion for our agents instead of benefit.
This function makes it appear as though the feedback submitted by agents is visible to admins, allowing them to improve Auto Assist configurations such as communication guidelines, instructions, and procedures. However, … this feedback is shared only with Zendesk and therefore cannot be used by admins.“
We ran into the same issue on my team and had to build a manual workaround to compensate: agents would tag tickets, take screenshots of incorrect internal guidance, and report it to admins.
It was slow and disruptive, so much so that we eventually stopped doing it consistently and relied on agents to raise only the most serious cases.
The ideal state is for agents to rate copilot replies and submit quick “what was wrong?” feedback that is visible to workspace admins in real time.
Even better, copilots should provide on-the-spot improvement suggestions the agent can choose from, beyond those thumbs up/down buttons (for example: “too generic,” “missing context,” “wrong policy,” “outdated info,” “tone mismatch”), and automatically route those signals into an internal reporting dashboard.
Report on “no-answer” copilot cases
Copilot tools usually offer decent reporting for customer-facing support automations. You can see which questions were successfully deflected, where the AI performed well, and which customer intents aren’t covered by your current content.
Similarly, most knowledge management tools provide out-of-the-box reporting on help center searches that returned no results, making it easy to spot content gaps and prioritize new articles.
But internal copilot tools are often missing these insights.
When an agent asks the copilot for guidance, and it can’t find anything useful, that’s a direct indicator of a knowledge gap and one of the most valuable signals you can capture. Yet, many copilots don’t provide reporting on these internal “no-answer” queries.
Instead, teams are forced back into manual processes, asking reps to flag gaps and report missing information to admins. That approach doesn’t scale, and it’s unreliable, as agents will typically only flag issues when they’re particularly painful.
Ideally, copilots should automatically log and report on unanswered queries for admins to review trends, identify the most common missing topics, and turn those insights into action, like new macros, updated help articles, stronger internal docs, or expanded source coverage.
Agent Copilots need additional settings for ticket summaries
When you’re taking over a troubleshooting-heavy ticket, the most valuable summary isn’t a full transcript in bullet form, but rather a quick snapshot of the original issue, any additional problems raised along the way, and a clear list of what’s already been tried.
But too often, copilot summaries default to a long “we said / they said” recap.
It’s technically accurate, but not quite useful. For example, I often end up skimming the thread myself because it’s faster than trying to extract the signal from an overly detailed summary.
What’s missing in many copilot tools today is flexibility in how summaries are generated. Support teams need multiple summarization modes or built-in prompts out of the box.
For example:
- Handoff summary optimized for another agent taking over.
- Troubleshooting summary focused on symptoms, context, and attempted steps.
- Leadership summary that highlights impact, risk, and resolution status.
- Bug report summary that captures reproducible steps, environment details, and key logs.
Yes, you can always copy the conversation into ChatGPT and ask for a custom format.
But unless your security team explicitly approves that workflow, it may not be an acceptable way to handle customer data. And even when it is allowed, it introduces extra steps and context switching that a copilot is supposed to remove.
Usage-based vs seat-based pricing
Beyond functionality, pricing is a major pain point with many copilot tools. Most require a per-agent license, which can feel reasonable for small teams but quickly becomes hard to defend in larger support organizations.
At scale, you can easily end up spending tens of thousands of dollars per year, which becomes hard to justify, especially when the copilot’s impact is limited to polishing writing or generating basic ticket summaries, without deeper learning, stronger integrations, or custom prompts.
And even if the tool is genuinely helpful, it rarely feels “worth another full-time agent” in measurable savings.
Usage-based pricing is often a better fit.
It aligns cost with real value, makes adoption lower-risk, and helps support leaders build a clearer ROI story where dollars spent correlate with replies generated, tickets assisted, and time saved.
It also avoids that “all or nothing” rollout pressure that seat-based models create.
Trials and free tiers can help here too, especially when procurement requires proof before approval.
Zendesk Agent Copilot, for example, offers a trial that can be requested through your named account manager (if your Zendesk plan includes one). That allows teams to validate value and collect internal metrics before committing to a larger contract.
Alternatively, Swifteq’s Zendesk Copilot Cowriter offers a free plan that includes 100 AI replies and 100 translations per month. When you’re ready to scale, there’s an unlimited plan for just $9 per agent per month.
Admins can also restrict access to selected team members to have better control over rollout, usage, and costs.
Supercharge Your Support Flow
Agent copilot tools can help your team move faster and handle tickets more efficiently. But you don’t have to stop at drafting replies and summarizing conversations.
You can push automation further with tools like Swifteq, which helps extend what Zendesk can do with add-ons like Zendesk Help Center Analytics and Zendesk Help Center Translate.
Ready to level up your support automation? Book a free demo and see how Swifteq can help you scale faster, tighten processes, and deliver a more consistent support experience.
Written by Maryna Paryvai
