Stop Optimizing Support Tickets: The Case for Shared Observability
Published: 27 February 2026
The IT service desk is the rhythmic heart of the modern enterprise. For decades, the “ticket” has been that heart’s pulse—a discrete, measurable unit of work. We measure ticket volume, time to resolution, and customer satisfaction scores based on these individual requests. IT leaders have spent millions optimizing this flow: implementing self-service portals, automating “tier 1” responses, and fine-tuning escalation paths.
But as our systems become more complex, more distributed, and—critically—more autonomous, the traditional support ticket is becoming a relic. It is a slow, reactive mechanism in an era that demands real-time, proactive intelligence.
At Aqon, we believe the next era of IT operations belongs not to “optimized ticketing,” but to Shared Observability.
The Problem with the Ticket: A Disconnect of Context
The fundamental flaw of the support ticket is that it represents a transaction, not a state.
When a user or a system monitor files a ticket, they are providing a snapshot of a problem that occurred in the past. By the time an IT professional (or even an automated script) opens that ticket, the context has changed. The logs have rolled over, the traffic patterns have shifted, and the “state” of the system is no longer what it was when the error occurred.
Furthermore, tickets create a “blind handoff.” The human-on-the-call explains the problem to a system, which then passes a text description to a human-on-the-desk. In an agentic environment, where AI agents are acting autonomously, this handoff becomes even more problematic. If an agent performs an action that triggers an alert, filing a ticket about it is a secondary, indirect way of communicating. We are losing the actual “eyes and ears” of the system in the translation to a textual task.
From Tickets to Shared Observability
Shared Observability is a new paradigm where humans and AI agents look at the exact same telemetry data in real-time, within a unified interface. Instead of a “queue of tasks,” the IT support team manages a “stream of state.”
In a Shared Observability model, the AI agent is not just a “solver” of tickets; it is an “interpreter of signals.” It sits “on-the-loop” with the human operator, monitoring the same dashboards, logs, and traces.
When an anomaly is detected, the workflow looks very different from the traditional ticketing model:
- Direct Surface: The agent doesn’t “file a ticket.” It surfaces the anomaly directly onto the Shared Observability dashboard, highlighting the relevant telemetry data.
- Contextual Reasoning: The agent provides an immediate, reasoning-based analysis of the situation: “I am seeing a 15% latency spike in the checkout service. This correlates with a recent configuration update in the API gateway performed by the Deployment Agent.”
- Collaborative Remediation: The agent suggests a fix (e.g., rolling back the config) and presents the impact analysis to the human operator. The human doesn’t have to “research the ticket”; they are already looking at the evidence and can approve the action with a single click.
The Benefits: Speed, Accuracy, and Learning
Moving from tickets to Shared Observability offers three transformative advantages:
1. Reduced Mean Time to Resolution (MTTR): By eliminating the “triage and research” phase of ticketing, issues are identified and understood in seconds rather than minutes or hours. The “hand-off” time between detection and remediation drops to zero.
2. Proactive “Self-Healing”: Because agents are monitoring the state in real-time, they can identify “pre-failure” conditions that would never trigger a traditional ticket. They can perform “preventative maintenance” (like optimizing a query or scaling a resource) before the user ever experiences an issue.
3. Enhanced Human Capability: In this model, IT professionals are no longer “ticket grinders.” They are “System Orchestrators.” They spend their time defining the health rules, refining the agent’s reasoning models, and handling the most complex, non-algorithmic problems that require high-level human judgment.
Implementing Shared Observability with Aqon
Transitioning to Shared Observability requires more than just a new dashboard; it requires a fundamental shift in how your data is collected, stored, and exposed. Your agents need high-fidelity, low-latency access to the same telemetry that your humans use.
At Aqon, we provide advisory services for the next generation of IT Operations (AIOps). We help organizations evaluate and design unified, agent-enhanced observability strategies that provide a clear view of their entire digital estate.
Is your IT team buried under a mountain of reactive tickets? Contact Aqon today to learn how we can help you implement Shared Observability and move your operations into the proactive, agentic era.
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