Dashboards provide a useful prism through which we can study the broader evolution of the IT professional’s role in the era of agentic AI. For years, dashboards have been the centerpiece of IT work, serving as the interface where teams interpret system behavior, diagnose issues, and plan actions. Dashboards epitomize the relationship between humans and their systems: humans observe, interpret, and act. As agentic AI enters the picture, that relationship begins to change. Let’s explore how.

The Dashboard: A Window Into the Old Model

For decades, the dashboard has been a defining element of IT operations. Teams monitor a constellation of metrics, including alerts, latency charts, wheels, gauges, levels, and usage graphs. Operations centers with walls of screens have become symbols of modern IT, epitomizing how we’ve worked until now.

  • Systems gather information
  • Humans interpret what it means
  • Humans decide how to act
  • Humans take required measures

The impressive visual complexity of dashboards has often masked an underlying inefficiency. Rows of graphs and gauges create an illusion of control, but they rely on humans to stitch together clues, interpret meaning, and translate visuals into action—work that is slow, manual, and prone to error, especially at scale. Today, tools like Pingdom® and PaperTrail™ already bypass dashboards by sending direct alerts that link to actionable insights. When configured well, these tools highlight exactly what matters without requiring a daily tour of every visualization. Agentic AI builds on this evolution. It shifts the balance from “humans monitoring systems” to “systems monitoring themselves,” surfacing only what truly requires human attention.

From Primary Interface to Secondary Oversight

Agentic systems don’t just gather data; they chain decisions together and take meaningful action. They can investigate incidents, validate assumptions, and propose or execute remediation steps with minimal human intervention. As these capabilities expand, much of the work dashboards were designed to support begins to shift to the agent itself. Consider a network outage. Historically, an IT professional would manually move between dashboards, check switches, trace connectivity, rule out ISP issues, and correlate anomalies. An agentic system can perform this work automatically. It checks resources, evaluates logs, correlates events, and presents the root cause. No dashboard needed to find the answer. The long-term vision expands this further. Agents don’t just identify problems; they propose fixes. A future alert may read: “Root cause identified. Here is the proposed remediation. Do you approve?” Instead of checking a hundred graphs, an IT pro might receive a single message: “Service health is bad.” From there, they simply ask, “Why?” and receive an answer in seconds.

Giving Systems a Voice

Does agentic AI make dashboards obsolete? Not entirely. Dashboards will remain, but their function will change. When the agent fixes issues autonomously, dashboards shift toward summarizing outcomes rather than supplying raw data. They will move from being primary analysis tools to becoming oversight mechanisms.

Dashboards evolved because systems were silent. They needed to be translated into visual signals. Humans are highly visual, but they are even more adept at language. When agentic systems can explain what they’re seeing, what they’ve done, and what they recommend next, the need for dense visual monitoring fades. In the near future, dashboards will be less about charts and more about performance insights, such as:

  • Time saved by agentic automation
  • Success rate of agent decisions
  • Frequency of human interventions
  • Patterns in remediation approval or rollback

Rather than showing what the system is doing, dashboards will show what the agent has accomplished. They will act as trust-building tools, allowing operators and managers to verify the system’s performance and make informed policy decisions about autonomy levels. Dashboards aren’t dying, but what they track will evolve.

The People Behind the Screens

Dashboards are evolving, but how will IT roles change alongside them? Every major technological shift reshapes jobs. When enterprise ERP systems, such as SAP, became mainstream in the early 2010s, they digitized paper-based workflows. Dozens of hands were once involved in processing expense reports, sorting files, reviewing entries, mailing approvals, and reconciling statements. After ERP adoption, those tasks shifted to scanning, reviewing, and verifying information digitally. The work didn’t vanish; it transformed. And new roles appeared. Today, we have specialists to maintain the system, troubleshoot issues, train teams, and manage integrations.

Agentic AI is likely to follow the same pattern. Early on, few roles are eliminated because teams won’t fully trust the technology. Instead, responsibilities shift. People transition from conducting manual tasks to verifying the agent's output, approving actions, and handling exceptions. Over time, as agent reliability improves, some functions may be streamlined, but new roles also appear around:

  • Supervising agent behavior
  • Setting policies
  • Supporting the underlying systems

Even outside IT, this pattern holds. A marketing team may adopt an AI system to automate repetitive tasks. The workload shrinks, but dependency on AI engineers grows. The agent won’t fix itself. Hence, even if a team reduces from 10 people to 8 or 9, an AI specialist may be added to maintain the system. The outcome is a rebalancing, not a replacement. Productivity rises, some tasks fade, and new roles emerge.

From Intervention to Oversight

Agentic AI shows that the real shift isn’t from humans to machines, but from manual interpretation to guided oversight. As systems take on more of the routine cognitive load, human judgment becomes more valuable, not less. Tools are becoming more conversational, more contextual, and more proactive, which means work becomes more about deciding and less about deciphering. The future of IT isn’t one where people are replaced, but one where people finally get to focus on the problems that matter. Dashboards helped humans understand their systems; agentic AI helps systems understand themselves — and that frees teams to operate at a higher level.

More in the Agentic AI series:

Forward-Looking Statements

This article contains forward-looking statements regarding future product plans and development efforts. SolarWinds considers various features and functionality prior to any final generally available release. Information in this article regarding future features and functionality is not and should not be interpreted as a commitment from SolarWinds that it will deliver any specific feature or functionality in the future or, if it delivers such feature or functionality, any time frame when that feature or functionality will be delivered.

All information is based upon current product interests, and product plans and priorities can change at any time. SolarWinds undertakes no obligation to update any forward-looking statements regarding future product plans and development efforts if product plans or priorities change.

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