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🛠️ IT Helpdesk Ticket Handling

A 600-person company handles about 1100 tickets per month with two level-1 agents who create, classify and route every ticket by hand, resolve simple requests, and rebuild a weekly Excel report from scratch. The redesign puts an intake and triage agent in front of the queue, lets a bounded resolution agent close documented requests end to end, and keeps humans on security paging and privileged access approval.

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The as-is: how it works today · 14 steps

The to-be: humans + AI agents · 14 steps, 6 AI-powered

Why this redesign

The bottleneck here is not resolution capacity, it is the manual gateway in front of the queue: 1100 tickets each pass through 6 minutes of hand triage, and misrouting costs two extra bounces plus a five hour first response. Moving intake, classification and routing to agents removes roughly 110 hours of level-1 work per month and collapses first response to minutes, because the requester gets a confirmation and a knowledge base suggestion instantly. The 40 percent of tickets with documented procedures become the automation core, but identity is the sensitive surface, so the resolution agent is fenced to an allow-list of standard-profile actions with a kill switch, and every elevated grant stops at a human approval gate as the constraint requires. Security incidents are pulled out by a deterministic rule before any agent can act, keeping the human paging promise intact. Level-2 is not automated; it is fed a diagnosis pack so specialists stop rediscovering documented fixes. With only two level-1 agents, the goal is not headcount reduction but shifting them to problem management and knowledge base curation, which is exactly the gap the weekly report will surface.

Quick wins of this redesign

• Turn on the intake and classification agent first: it removes 6 minutes of manual triage on 1100 tickets per month with no identity risk and no change to who resolves what.
• Automatic status updates at intake, assignment and closure eliminate the email chasing that currently interrupts both level-1 and level-2.
• Attach the top three knowledge base articles to every escalated ticket so level-2 stops re-solving documented issues.
• Replace the manual Excel report with an agent-built draft the service lead only reviews and comments.

Risks to manage

• The resolution agent acting in Azure AD could grant access beyond the intended profile if group naming is inconsistent, so the allow-list must be curated before go-live.
• Classification quality depends on historical ticket data that is itself inconsistent, which can bake existing misrouting patterns into the model.
• Confluence articles are unevenly maintained, so grounded answers may confidently cite outdated procedures unless article freshness is checked.
• Automating the easy 40 percent leaves level-1 with only hard cases, which can raise stress and attrition if the role is not redefined.

Suggested pilot plan

Run a six week pilot on the two highest volume documented categories, password reset and standard-profile access, with the two level-1 agents and one level-2 specialist as reviewers. Weeks 1 and 2: the triage and resolution agents run in recommend mode only, proposing classification and the fix while humans execute, to measure agreement. Weeks 3 to 6: switch the resolution agent to acts within the allow-list, keep the approval gate on every elevated grant, and sample 30 tickets per week plus all overrides. Success after roughly 500 pilot tickets: routing accuracy above 90 percent, median first response under 10 minutes, at least 35 percent of pilot volume closed without human touch, zero unapproved access grants and zero security incidents handled by an agent. If any of the last two fail, disable the resolution agent and stay in recommend mode.