Modern operations teams don’t suffer from a lack of tools—they suffer from too many handoffs. A single incident ticket might require log checks, access validation, configuration updates, approvals, user communications, and documentation updates across multiple systems. Traditional automation helps in isolated steps, but work still gets stuck between queues, approvals, and context switching.
A large enterprise service organization was managing high-volume IT and business operations across infrastructure, applications, end-user computing, and shared services. Even with an ITSM platform, knowledge base, and automation scripts, the operational model was still ticket-driven and human-dependent.
Key issues included:
High ticket-to-resolution latency: Many tickets were straightforward, but resolution time was stretched by manual coordination—triage, routing, clarifying questions, and waiting for approvals.
Repetitive work across tools: Agents had to switch between ITSM, identity systems, monitoring tools, CMDB, endpoint management, email, and collaboration tools to complete common tasks.
Inconsistent outcomes: Similar tickets were resolved differently depending on who handled them, creating variability, rework, and audit gaps.
Knowledge trapped in teams: Resolution know-how lived in people and chat threads rather than reusable workflows.
Automation fatigue: Legacy RPA and scripts existed, but they required manual triggering and didn’t adapt well to exceptions or missing context.
Governance constraints: The organization needed tight control—no “free-running AI.” Any execution required approvals, logging, and safeguards.
The client’s goal was clear: reduce operational load, improve consistency, and move from ticket handling to outcome delivery—without losing compliance, security, or oversight.
Maayan Technologies implemented an Agentic AI operations layer integrated with the client’s ITSM and core operational tools. The solution combined LLM-based reasoning with deterministic controls, tool permissions, and human-in-the-loop approvals to ensure safe execution.
1) Agentic Workflow Orchestration (“Plan → Act → Verify”)
Instead of responding with advice, the agent followed an execution pattern:
Understand: interpret ticket intent, extract entities (user, asset, app, location, urgency), and identify required actions.
Plan: generate a step-by-step resolution plan aligned to approved runbooks and policies.
Act: execute tasks through connected tools (APIs, scripts, automation platforms) within permission boundaries.
Verify: confirm results via monitoring checks or system queries before closing actions.
Document: update the ticket with actions taken, evidence, and final status.
This ensured the AI agent consistently moved work forward, not just conversations.
2) Tool Connections with Guardrails
We connected the agent to the enterprise toolchain (as permitted), such as:
ITSM ticketing and workflow updates
Identity and access management (account unlocks, group access requests)
Endpoint management (software install, policy refresh, device health checks)
Monitoring and logs (alert triage, service checks)
CMDB and asset inventory (context for routing and impact)
Knowledge base (runbook retrieval and guided execution)
Guardrails ensured execution stayed controlled:
Role-based access and scoped credentials
Allow-listed actions (“safe operations”) and blocked operations (“high risk”)
Approval gates for privileged actions (e.g., access changes, production config updates)
Rate limits and rollback steps for changes
Full audit logging of every tool call and outcome
3) Human-in-the-Loop Where It Matters
The agent auto-executed only when confidence and risk policies allowed. For ambiguous issues or sensitive operations, it:
asked targeted questions to reduce back-and-forth,
recommended the best action with evidence, and
requested approval from an authorized human before executing.
This improved speed without sacrificing accountability.
4) Runbook-to-Agent Conversion
We converted common operational runbooks into agent-ready workflows. Examples included:
Password resets and account unlocks
VPN and email profile remediation
Software deployment and patch verification
Disk space remediation and log cleanup
Service restart workflows with health checks
Standard access provisioning with policy validation
This created a repeatable “library of outcomes” that scaled across teams.
5) Measurement & Continuous Improvement
We introduced metrics to measure real operational impact:
Ticket deflection vs execution rate
First-time-right resolution
Mean Time to Acknowledge (MTTA) and Mean Time to Resolve (MTTR)
Reduction in reassignments and clarifying interactions
Agent productivity and backlog reduction
Compliance/audit pass rate for executed changes
Dashboards highlighted failure patterns, automation opportunities, and policy refinements.
The Agentic AI rollout delivered outcomes beyond a typical chatbot or automation project:
Faster ticket-to-resolution by eliminating manual handoffs and tool switching.
Higher consistency and quality through runbook-driven execution and verification steps.
Reduced operational load as the agent handled common requests end-to-end or prepared execution-ready plans for engineers.
Improved user experience with quicker acknowledgements, clear updates, and fewer “please provide more details” loops.
Stronger governance via approvals, scoped permissions, and complete audit trails.
Scalable “Outcome Library” that expanded over time, adding new workflows without rebuilding the architecture.
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