As of
Add an AI agent to your ITSM tool, or replace it?
Add an AI agent on top of your current ITSM tool if the tool works well and your data in it is accurate; replace it if the tool itself is what slows your team down. Most enterprises end up doing both in sequence: they run an AI-native platform alongside the current one, let the agent start resolving requests, and move the queue over once it matches or surpasses the old tool on the metrics that matter. That avoids a risky cutover and gives you real results to judge before the old contract renews.
An AI agent is software that understands requests in plain English and acts on them, such as resetting a password or granting access, within the rules IT sets, rather than only sharing links to knowledge articles and answering questions.
This guide compares the three options and how to think about deciding between them.
The three options
| Option | What it means | Works well when | Watch out for |
|---|---|---|---|
| Add an agent on top | An AI agent answers and acts in front of your existing ITSM platform | The platform fits how you work, and its data (assets, access, knowledge) is current | The agent can only be as good as the data and assets underneath it |
| Replace the platform | Move tickets, workflows and knowledge to a new platform | The current tool is the bottleneck: slow to change, expensive to run, poorly adopted | A big-bang cutover risks disruption if it is not staged |
| Run both, then move | Run an AI-native platform alongside the current one, then move the queue gradually | You want results before committing, or a renewal is coming up | A short overlap of two tools, so set a clear decision date |
The questions that decide it
Is the problem the interface or the platform underneath?
If employees just want to ask in plain English, and your team is happy with the tool behind the portal, an agent in Slack and Microsoft Teams may be enough. If your team also struggles with the tool itself, an agent on top only hides the problem from employees. Replacing the platform changes your IT team's experience too: the tickets that need a person arrive already investigated, with a suggested fix and a one-click action to apply it.
How accurate is the data the agent would use?
An agent resolves requests from what it knows about people, devices, apps and access. If that comes from a CMDB your team maintains by hand, the agent inherits every gap in it. Ask what it would know about an employee before their first message, and where each fact comes from. See what an anti-CMDB is.
Who changes the workflows?
An agent on top answers employees, but the work behind each request still runs on your current tool's workflows. If adding an approval step or changing a request form takes an admin or a consultant today, it still will. On an AI-native platform, your team describes the change in plain English and reviews it before it goes live.
Do you want to go beyond IT?
An agent on top of an IT tool stays inside IT. If you want HR, Legal and Finance requests handled the same way, with one place for employees to ask and one picture of the company behind every team, the platform itself has to cover those teams. That is a reason to replace rather than add on top.
When does your current contract renew?
A renewal date is the natural deadline. Running an AI-native platform alongside the current one for the months before renewal gives you real leverage and metrics to bring to that decision.
How STLabs fits
STLabs is a full-stack service desk with the agent built in, not an agent layered over another platform, and it can run alongside your current platform before taking on more.
- It connects to ITSM platforms including ServiceNow, Jira Service Management and Freshservice, with two-way sync. See what STLabs integrates with.
- Axiom™ builds its picture of people, devices, apps and access from your integrations, so there is no CMDB to maintain and update by hand.
- Workflows are described in plain language and saved as versioned code you can review and roll back. See Workflow Automation.
- IT, HR, Legal and Finance share one platform, so most teams start in IT and add the others when they are ready.
For what the move involves, see moving to STLabs from your current service desk, and for more on the differences, how STLabs compares to traditional ITSM tools.
Frequently asked questions
Can an AI agent work with ServiceNow, Jira Service Management, or Freshservice?
Yes, an AI agent can run alongside them or replace them. STLabs syncs both ways with ServiceNow, Jira Service Management, or Freshservice, so a ticket created or updated in either tool shows up in the other, and your team keeps working where it is while the agent resolves requests. It also reads your ticket history, categories and knowledge base so the agent learns from how your team already works.
How long does it take to see results?
With legacy platforms: Days if the platform connects to the systems you already run, months if it needs a CMDB filled in by hand. With STLabs, teams go from first integration to real results in five days: you connect your systems on day 1, Axiom™ builds its picture of your company on days 2 and 3, and workflows are resolving requests by day 5.
What happens to ticket history if we switch?
A good migration brings it with you. When you move to STLabs, open tickets, incidents and requests come over with their categories and SLAs, along with your knowledge base articles. Closed tickets stay searchable, and the agent learns from how they were resolved.
How do we test a new platform before committing?
Run it alongside your current tool on a few common requests and compare the results with your baseline. STLabs runs next to the tool you have, starting with requests such as password resets and software access, and its analytics dashboards show what was resolved and how fast from the first day, so you decide with your own numbers. For what else to check, see how to evaluate AI-native service management, or book a demo and we will map your systems before the call.