$20M+ projected annual capex saving from agentic IT support operations

A business case for agentic IT support, modelled for a Tier-1 enterprise with a 120+ person support ops team, with six agents covering the full ticket lifecycle on Gemini Enterprise.

Outcome

Modelled for a Global technology leader: $20M+ projected annual capex saving, $2M saved a year, about 50% lower MTTR, 2x engineering team productivity, and 25 to 40% lower cost per ticket.

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At a glance

Client & industry

Global technology leader, modeled on a support operations team of 120+

Engagement

Managed agent-as-a-service on Gemini Enterprise for custom and hybrid stacks, billed only on tickets resolved

Coverage

Ticket intake and triage, investigation and RCA, knowledge lookup and auto-resolution, lifecycle governance, and engineering support

Differentiators

Multi-agent and native to Vertex AI and Gemini, ITSM-agnostic, covering custom, legacy, and cloud systems, with native MCP and A2A

Maps to below services

Agentic AI & AgentOps

Where it started

L1 and L2 support running on manual, siloed workflows that cost hours on every ticket in triage, rework, and unnecessary escalations.

Business problem

Manual triage forced agents to hand-read and route every ticket, which was slow, error-prone, and impossible to scale. Misrouted tickets bounced between queues, blowing past SLAs and stretching first-touch times as volumes grew. Up to 40% of tickets were repeat, self-resolvable issues that still landed on human agents, creating chronic backlog and burnout.

Technical barriers

Routine tickets took hours to days to resolve. Superficial fixes kept tickets reopening, engineers were pulled into L1 and L2 escalations daily, and a 1,000+ hour MTTR tail of zombie tickets sat with no governance.

Agivant's role

Agent design across the ticket lifecycle, platform architecture on Gemini Enterprise, and the business case model

What Agivant built

Agivant designed six agents mapped to the ticket lifecycle on an agentic IT support platform. An orchestration layer runs alert-triggered workflows and agent mapping on Agent Engine with the A2A protocol. An AI intelligence layer handles issue detection, resolution matching, duplicate finding, and RAG with Gemini models. Underneath, a context engine on Gemini Enterprise brings in 100+ pre-built connectors, with native MCP via ADK and real-time and batch ingestion. Value in days and weeks.

Triage and categorization agent

Eliminates duplicate tickets, gathers context autonomously across systems, removes ping-pong handoffs, and brings triage time down to 15 minutes.

Investigate agent

Captures the true reason for a failure immediately, prevents premature bug closure on transient issues, and blocks 80% of superficial fixes from reopening.

Recommend agent

Replaces the manual investigation phase after RCA and surfaces fix paths from internal knowledge sources.

Auto resolve agent

Recommends validated remediation steps and executes them with a human in the loop.

LifecycleOps agent

Keeps the queue clean, caps maximum ticket open time, identifies and closes transient tickets, and prevents ticket abandonment across the 1,000+ hour MTTR tail.

Support ops assistant agent

Gives support teams instant answers without waiting on engineering, accesses documentation autonomously, and brings engineering-dependent resolution time down to minutes.

Agent governance

Agent registry, agent identity, agent gateway, governance policies, and AI threat and vulnerability scanning across every agent.

How it is built

Orchestration	Intelligence	Knowledge layer	Context	ITSM	Observability	Governance
Agent Engine with A2A protocol, alert-triggered workflows, agent mapping	Gemini models, RAG engine	Hybrid search index, knowledge graph of tickets, content, and relationships	Gemini Enterprise with 100+ pre-built connectors, native MCP via ADK	ServiceNow, Jira, and any other ITSM	Cloud Operations Suite, Cloud Logging	Agent registry, agent identity, agent gateway, governance policies, AI threat and vulnerability scanning

Before

MTTR of hours to days for routine tickets, manual triage with ping-pong handoffs and duplicates, engineers pulled into L1 and L2 escalations daily, high reopen rates from superficial fixes, and zombie tickets with a 1,000+ hour MTTR tail.

After

MTTR in minutes through autonomous triage and RCA, autonomous context gathering and deduplication, a support assist agent answering without escalation, root-cause fixes that stay fixed, and a lifecycle agent that caps open time and cleans the queue.

Proof in production

A ticket arrives and the triage agent removes duplicates, gathers context from connected systems, and routes it within 15 minutes. The investigate agent captures the true root cause, the recommend and auto resolve agents surface the fix path and run the validated steps with a person approving, and the lifecycle agent closes anything transient before it ages. Support teams get answers from the assistant agent without waiting on engineering, and true root-cause capture ends the fix-it-again-next-week cycle.

Measured results

Modelled for a Tier-1 enterprise with a 120+ person support ops team. Engineers get their week back for product work, and the same SLAs hold at a fraction of the per-ticket cost.

$20M+

Projected annual capex saving

$2M

Saving per year

~50%

MTTR reduction

2x

Engineering team productivity

25 to 40%

Lower cost per ticket

Up to 80%

Fewer ticket reopens

The repeatable pattern

Reusable asset

The agent library runs as a managed agent-as-a-service on Gemini Enterprise, built for Google Cloud Marketplace, ITSM-agnostic, and native to MCP and A2A, so the same six agents fit custom, legacy, and cloud stacks and plug into ServiceNow, Jira, or any other ITSM. Every build amplifies the next.

Challenges overcome

Unifying context from documents, forums, ITSM, runbooks, and observability data, removing duplicate tickets before they spread across queues, and keeping every executed fix under human approval.

Enterprise movement

Foundational

Operational

Path to autonomous