L1/L2 Automation | Agentic AIOps
Stop Staffing Tickets. Start Preventing Them.
Most L1/L2 effort goes into triage, not resolution. QyrusAI reads every ticket, log, and signal in context, diagnoses root cause in minutes, and resolves routine incidents autonomously — turning your support layer from a cost center into a funding source for modernization.
Challenges
The L1/L2 Problem
Every Enterprise Knows.
Support tiers are drowning in volume they shouldn't have to touch. Discover how replacing manual triage and SME-dependent diagnosis with a knowledge-graph-driven agentic layer turns L1/L2 from a headcount problem into a self-resolving system.
Triage Without Context
Tickets are routed on keywords and gut feel, not on how the underlying applications and infrastructure actually connect - so the wrong team ends up owning the wrong problem.
The Same Tickets, Over and Over
Without a system that learns from resolutions, L1/L2 keeps re-solving the same class of incident — ticket volume never actually shrinks.
Diagnosis That Depends on People
Root cause analysis relies on fragmented historical tickets and SME memory, consuming 1 to 10 hours per incident before a fix is even attempted.
Escalation as the Default
When L1 can't resolve it, it moves to L2, then L3 — not because the issue is complex, but because no one has the context to stop the handoff.
How it works
Discover the Estate. Detect the Signal.
Diagnose the Cause. Remediate Autonomously.
Explore how QyrusAI compresses the ticket lifecycle — from first alert to validated fix — using a live Knowledge Graph and agentic AI workflows that operate with intent, not just instructions.
VMs, networks, and apps tracked live
QyrusAI builds a real-time inventory of VMs, networks, and applications across the full estate without any manual audit process. As infrastructure changes, the inventory updates automatically rather than going stale between review cycles. This continuous baseline is what every downstream detection and diagnosis capability depends on being accurate.
Recurring tickets grouped automatically together
Historical ticket data is analyzed alongside infrastructure discovery to cluster repeated incidents and service requests as they emerge. Instead of each ticket being handled as a one-off, patterns across tickets become visible immediately. This clustering is what first reveals which issues are isolated events and which are symptoms of a recurring, unresolved problem.
Common requests identified before they repeat
Beyond incidents, QyrusAI tracks patterns in routine service requests to identify which ones recur often enough to warrant automation. This turns request-handling from a purely reactive queue into a dataset that reveals its own optimization opportunities. Recognizing the pattern is the first step toward eventually automating the response.
The estate stays current by design
Unlike a periodic inventory audit, discovery here runs continuously, so the estate is observed as it changes rather than surveyed at fixed intervals. No manual inventory process needs to be scheduled, maintained, or trusted to still be accurate months later. This is the foundation that lets everything downstream — detection, diagnosis, remediation — operate on current, not stale, data.
Unusual patterns flagged before escalation
QyrusAI applies predictive and descriptive analysis to identify anomalies across the estate as they occur, rather than waiting for a threshold breach to trigger a manual alert. This catches emerging issues while they’re still small deviations, not yet full incidents. Early detection here is what shifts the team from reacting to problems to catching them forming.
Related signals grouped, not scattered
Rather than treating every alert as an isolated event, QyrusAI correlates related signals across systems to reveal the underlying pattern connecting them. A single root cause often triggers a dozen disconnected-looking alerts, and correlation is what reunites them into one actionable signal. This is what prevents a team from chasing symptoms one alert at a time.
Issues anticipated before they escalate
Descriptive and predictive analysis are used to anticipate incidents before they fully materialize, based on patterns the platform has already learned from historical data. This gives teams a window to intervene proactively instead of waiting for an incident to formally open. Prediction is what turns AIOps from a faster response mechanism into a genuinely preventive one.
L1 agents see only what matters
Non-actionable alerts are filtered out before they ever reach an L1 agent, so the team’s attention goes only to signals that genuinely require a response. This directly addresses “alert fatigue” — the state where so many false positives arrive that real issues get lost among them. Filtering at this stage is what makes proactive, prioritized triage possible instead of reactive alert-chasing.
Dependency data added to every incident
Each incident is automatically enriched with dependency context pulled from the Knowledge Graph, rather than requiring an analyst to piece that context together manually. This means an agent investigating an issue immediately sees what it’s connected to and what it might be affecting. Context that used to require tribal knowledge is now attached to the ticket from the moment it’s created.
Hours of RCA compressed to minutes
QyrusAI compresses a manual root cause analysis process that traditionally takes 1–10 hours down to under 30 minutes. This isn’t a modest efficiency gain — it changes what’s operationally realistic during an active incident. Diagnosis that used to require pulling in a senior engineer can now happen automatically, in the time it takes to read the ticket.
Solutions suggested, not just causes
Beyond identifying what’s wrong, QyrusAI generates prescriptive solution paths — specific, recommended next steps rather than just a diagnosis. This closes the gap between “here’s the problem” and “here’s what to do about it,” which is often where manual troubleshooting stalls. A prescribed path is what lets a less senior agent act with the confidence of a more experienced one.
L2/L3 handoffs no longer required
With root cause and a prescribed solution path already in hand, L1.5 agents can resolve issues that previously required escalation to L2 or L3 specialists. This isn’t about replacing deep technical expertise — it’s about not needing it for problems that don’t actually require it. Reducing unnecessary escalation is what shortens the ticket lifecycle most directly.
Routine incidents resolved without a human
QyrusAI executes self-healing workflows automatically for routine incidents, closing tickets without requiring a human to manually run each step. This is reserved for well-understood, lower-risk issues where the resolution path is already validated. Automating these frees the team to spend their attention on the incidents that genuinely need human judgment.
Remediation tested in simulation first
Remediation workflows are generated and validated through Digital Twin simulation before they’re ever run against production. This means the fix has already been tested against a live model of the environment, not just assumed to work. Validating in simulation first is what makes autonomous execution safe enough to trust with real infrastructure.
Oversight preserved where it matters
Autonomous remediation operates with human-in-the-loop governance, so higher-risk actions still require explicit approval before execution. This isn’t full automation without oversight — it’s automation that knows the difference between routine and consequential. Governance here is what makes autonomous remediation something an enterprise can actually deploy with confidence.
Detection to resolution, fully closed
QyrusAI closes the loop from initial detection to confirmed resolution, verifying that a remediation actually fixed the underlying issue rather than just executing a script and assuming success. This confirmation step is what separates genuine autonomous resolution from automation that quietly leaves problems half-fixed. Nothing in this pipeline waits on a human to manually confirm the loop is closed.
Core Features
Built to Turn Support Tiers Into
a Self-Resolving System.
Stop measuring L1/L2 by headcount and ticket backlog. Discover how a live Knowledge Graph, agentic remediation, and continuous learning turn routine support into an autonomous, self-improving layer.
Intelligent Ticket Triage
Automatically detects assignment group, priority, and intent for every incoming ticket - eliminating the manual "triage hop" and routing loss.
Knowledge-Graph RCA
Enriches every incident with dependency context, compressing root cause analysis from hours to minutes and reducing SME dependency.
Agentic Auto-Remediation
Executes governed, human-in-the-loop AI workflows to resolve L1/L1.5 incidents autonomously, validated against Digital Twin simulations.
Continuous Learning Loop
Feedback mechanisms like the KB Nudger and Incident Summarizer refine agent knowledge with every resolution — shifting the mission from ticket resolution to ticket prevention.
A GLIMPSE ON THE NUMBERS
Resolve Faster. Escalate Less. Fund What's Next.
These aren’t projections — they’re the measurable result of moving triage and diagnosis out of manual workflows and into a governed, agentic loop.
cost reduction in L1/L2 activities
Improvement in MTTR
reduction in routine operations effort
improvement in CSAT scores
Continuous testing. Continuous learning. Continuous value.
Join leading enterprises
driving the loop forward.
CUSTOMER TESTIMONIALS
Enterprises running the loop
in their own words.
Real Teams. Real Outcomes. Real Confidence in what ships next.
Qyrus not only supports the testing of our Web, Mobile, and API components as part of our CI/CD processes, but also in ongoing regression-testing across our partner ecosystem. The real power of Qyrus is that we have this extremely broad testing capability in one tool, run in the cloud, and reusable across all our development teams.
The transition to Qyrus has marked a significant turning point for us. By embracing automation, we not only streamlined our SAP test automation process but also achieved a remarkable reduction in overall project testing time by 40%.
... the Qyrus platform have been a great addition to Monument's product delivery capabilities. Within a few months, we have been able to create a comprehensive test suite of complex end-to-end test scenarios spanning multiple platforms and channels. The Quinnox team has helped us embed the Qyrus solution, and the supporting processes around it, into our agile delivery methodology.
Resources
Dive into our latest research and implementation guides. See how a live Knowledge Graph paired with agentic remediation turns support-tier automation into a funding mechanism for broader modernization.
Blog
October 15, 2026 | 
4 min
Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It
Read More
October 14, 2026 | 5 min
The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value
Read More
October 9, 2026 | 8 min
Application Discovery and Dependency Mapping: The First Step Before You Modernize Anything
Read More
Blog
October 15, 2026 | 4 min
Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It
Read More
October 14, 2026 | 5 min
The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value
Read More
October 9, 2026 | 8 min
Application Discovery and Dependency Mapping: The First Step Before You Modernize Anything
Read More
Case Study
Reports
Whitepaper
Move From Ticket Resolution to Ticket Prevention.
30-minute walkthrough with a solutions architect — we’ll show you where your L1/L2 volume is coming from, and how QyrusAI would resolve it autonomously today.
