How to add AI agents to HaloITSM

Adding AI agents to HaloITSM means connecting an AI layer through a webhook and the Halo REST API, authenticated with OAuth client credentials. The AI classifies incoming tickets, searches your knowledge base for a matching answer, and writes the result back to the ticket: a private note for the team, or a public reply once a category is trusted enough to run on its own. You keep HaloITSM exactly as it is. No migration, no plugin, nothing new for your team to learn.

HaloITSM has grown fast for a reason. MSPs and mid-size IT teams like it because the workflow designer is genuinely easy to configure, and the platform covers ITSM, asset management, and a service catalog in one place. An AI agent layer sits on top of that foundation and does the part workflows cannot do on their own: read a messy sentence and work out what it actually means.

Why add AI agents on top of HaloITSM?

Halo's own workflow engine already handles a lot: routing rules, SLA escalation, approval steps, automated actions triggered by ticket status. That is genuine, useful automation, and worth keeping exactly as configured.

What a workflow cannot do is read intent. A rule can match the word "VPN" in a subject line. It cannot work out that "I can't get in from home since the update last night" describes a VPN problem when the word VPN never appears. That is the gap an AI agent fills: it reads the ticket the way a first-line engineer would, decides what it is actually about, and only then applies the category, priority, and resolver team that Halo's workflows can act on.

For an MSP running several clients through one Halo instance, this matters even more. Each client tends to phrase the same handful of problems differently, and an AI agent that reads for meaning generalizes across accounts in a way a fixed rule set per client does not.

How does the integration work?

The connection runs on a webhook and Halo's REST API:

1. A ticket event happens in Halo. A ticket is logged, updated, or gets a new action (Halo's term for a note or reply).
2. Halo fires a webhook. The ticket data (summary, details, status, ticket type, client) is sent to ITSM Autopilot.
3. AI agents process the ticket. Within seconds the AI reads the full context, classifies category, priority, and team, and searches your knowledge base and past resolutions for a matching answer.
4. Results go back through the Halo API, authenticated with OAuth. ITSM Autopilot connects with a client ID and secret using OAuth client credentials, no personal login involved. Classification lands on the ticket fields. Suggested answers are added as private notes for your team. When a category has been enabled for autonomous handling, the reply is posted to the requester instead.

Setup is a short, one-time configuration: create the OAuth client in Halo, connect it in ITSM Autopilot, and point one webhook at the ticket events you want covered. Nothing is installed inside Halo itself, and the connection can be revoked at any time.

For a broader look at how this pattern applies across ITSM platforms, see our integration guide. The same approach connects to ServiceNow, with the platform-specific details swapped out.

What do the AI agents actually do?

• Triage. Category, priority, and resolver team are set within seconds, using the exact values configured in your Halo instance. No invented categories.
• Clarification. A ticket like "printer broken" gets one reply asking the two or three questions a first-line agent would ask, in the requester's own language.
• Knowledge answers. When the knowledge base, an uploaded document, or the service catalog contains the fix, the agent drafts an answer and cites the source.
• Sentiment watch. A frustrated, urgent, or anxious ticket is flagged to a human immediately, wherever it sits in the queue.
• Knowledge curation. Every resolved ticket becomes a candidate known-error article, so the same problem is answered faster the next time it comes in.

Start in shadow mode, not in production

Do not switch on public replies on day one. Start in shadow mode: the AI processes every ticket, but every result lands as a private note. Nothing reaches a requester until you decide it should.

After a week or two in shadow mode, you can see exactly which ticket types and categories the AI handles well. Turn on autonomous resolution for those first, keep the rest in shadow, and expand as the numbers earn it.

Frequently asked questions

Does this replace Halo's own automation and workflows?

No. Halo's workflow engine keeps doing what it already does well: routing, SLA timers, approvals, and scheduled actions. ITSM Autopilot adds a layer that reads intent and drafts an answer before those workflows take over. It does not replace any part of Halo.

Which Halo plan or setup do I need?

Any HaloITSM instance with API access and the ability to create an OAuth client, which is standard on a Halo tenant. No separate add-on is required.

Is this suitable for an MSP with multiple clients on one Halo instance?

Yes. The AI reads each ticket on its own terms rather than relying on a client-specific rule set, which tends to generalize well across the varied phrasing that different clients use for the same handful of common issues.

What happens if the AI is not confident about an answer?

Every outbound action has a confidence threshold. Below it, the AI does not reply to the requester. It leaves its analysis as a private note instead, and a human decides from there.