What Practitioners Really Want from an AI Agent in IT

TL; DR

Practitioners don’t want “AI for AI’s sake”—they want an agent that can explain complex incidents clearly with concise, contextual issue summaries.

Root cause assist is critical because it turns mountains of telemetry and correlated alerts into a short list of plausible causes and contributing factors, dramatically accelerating MTTR.

Guided troubleshooting is where trust is won: step-by-step, environment-aware paths that help teams move from operator to orchestrator without surrendering control.

From ‘AI for IT’ Hype to Real Incident Help

If you spend your days in major incident huddles, Slack bridges, and 3 a.m. incident calls, you’ve already seen that “AI for IT” can mean just about anything from fancy dashboards to noisy anomaly alerts that add more confusion than clarity. What practitioners actually need is much more specific: an AI agent that sits in the middle of messy, cross-domain incidents and helps them understand what’s happening, why it’s happening, and what to do next.

During our recent SolarWinds Day: Your Path to Autonomous Operational Resilience, we asked hundreds of attendees across APJ, EMEA, and NA/LATAM which AI agent capabilities would make the biggest real-world impact. Their answers weren’t vague or theoretical. They consistently chose guided troubleshooting, issue summaries, and root cause assist, often in combination with context-aware Q&A and safe, guarded actions.

This blog breaks down why those three capabilities matter so much, what they look like in practice, and how we’re building them into the SolarWinds AI agent (SW1) to support practitioners—not replace them.

What Practitioners Told Us: The AI Features That Actually Matter

When we asked SolarWinds Day attendees, “which feature of the AI agent would have the most immediate impact on your team?” the same options surfaced again and again:

• Guided troubleshooting
• Issue summaries
• Root cause assist

Often, these were selected together, alongside context-aware Q&A, alert grouping, and safe actions (guardrails)—a strong signal that practitioners want an AI agent that can:

1. Make sense of noise (grouping and summarizing alerts),
2. Point to likely causes (root cause assist), and
3. Walk them through the fix safely (guided troubleshooting with guardrails and approvals).

In parallel, many respondents said their primary IT operations focus for 2026 is AI & automation, often paired with cost reduction and security—context that explains why they’re pushing so hard for practical, high-leverage AI capabilities, not just another dashboard.

In short: practitioners are telling us, very clearly, what they expect from an AI agent. Our job is to ensure SW1 meets those expectations.

From Alert Floods to Clear Narratives: Why Issue Summaries Matter

During a major incident, practitioners are rarely short on data. They’re drowning in it: alerts, traces, logs, tickets, change events, and chat threads. The limiting factor is cognitive capacity, not telemetry.

That’s why issue summaries ranked so highly. Practitioners want an AI agent that can:

• Ingest raw signal: alerts across infrastructure, network, applications, and services.
• Understand relationships: dependencies, recent changes, known issues, and past incidents.
• Produce a compact narrative that explains:
• What’s impacted
• When it started
• The likely blast radius
• Key symptoms and patterns worth investigating

Instead of every engineer reconstructing the story manually from their own subset of tools, the agent becomes the shared “first draft” of the incident timeline. That helps:

• Reduce time-to-context for new responders joining a bridge call.
• Keep stakeholders aligned on what we know vs. what we suspect.
• Prevent teams from chasing duplicate or already-disproven theories.

In SW1, this takes the form of concise, context-aware incident summaries that pull from observability data, tickets, and prior incidents, helping teams get aligned quickly and communicate clearly with business and security stakeholders.

Root Cause Assist: Turning Telemetry into Hypotheses

Even experienced SREs and network engineers can lose precious time sifting through conflicting indicators. That’s why root cause assist stood out as a top-priority capability in the SolarWinds Day polls, often selected on its own or paired with guided troubleshooting and summaries.

Practitioners don’t expect the AI agent to magically “know” the root cause. They want it to:

• Prioritize likely causes based on correlated anomalies, recent changes, dependency graphs, and historical incidents.
• Explain its reasoning in human-readable language, not opaque scores or unexplained “confidence” metrics.
• Surface related evidence, such as:
  • 95% of affected requests share this service dependency.
  • CPU saturation began immediately after this deployment.
  • Similar patterns were seen in incidents on these dates.

Within the SolarWinds Platform, our AIOps capabilities, anomaly detection, dynamic thresholds, alert correlation, and Root Cause Assist provide the signal layer. SW1 then acts as the agent layer on top of that: interpreting those signals in context and turning them into testable root-cause hypotheses practitioners can validate or discard.

The result isn’t “push-button incident resolution,” but a much shorter path from “something’s wrong” to “we’re pretty sure this is why.”

Guided Troubleshooting: From Operator to Orchestrator

Another strong pattern from SolarWinds Day: many practitioners said their role is already shifting, or starting to shift, from “operator” to “orchestrator.” The work is less about clicking through every screen and more about deciding which actions matter and in what order.

That’s exactly where guided troubleshooting comes in. The ideal AI agent doesn’t just say, “you might have a network bottleneck.” It helps you work the problem:

• Suggesting next-best questions to ask of your data (“check latency between these two services” or “compare error rates before and after the latest deploy”).
• Recommending structured triage steps, prioritized by impact and effort.
• Tailoring guidance to your environment, not generic runbooks copied from a vendor blog.

In practice, practitioners told us they want this guidance everywhere they work:

• When onboarding new devices, apps, or cloud resources.
• When building or modifying modern dashboards.
• When tuning alerts to reduce noise without missing critical issues.
• When learning new features or expanding into adjacent domains like ITSM or incident response.

In SW1 today, that means read-only, environment-aware guidance: the agent can query observability data, documentation, and configuration details to recommend steps, but it doesn’t take action on your behalf. Over time, we’re expanding toward safe actions with clear guardrails and human oversight, which practitioners also flagged as high-value features.

Safety, Control, and Trust Are Non-Negotiable

SolarWinds Day chat and Q&A made it clear: enthusiasm for AI is real, but so are concerns about security, control, and compliance. Attendees asked:

• Does the AI agent need internet access, and can it be turned off?
• Is customer data used to train underlying models?
• Can we control who gets access and what actions the agent is allowed to perform?

In our current SW1 preview for Observability Self-Hosted, the agent is deliberately scoped to earn that trust:

• It’s read-only: SW1 helps you investigate, summarize, and guide, but does not perform actions on your environment yet.
• On-premises deployments connect via Platform Connect, with clear enable/disable controls so customers can keep it off if needed or limit connectivity paths.
• Only the minimal, relevant context needed for a given prompt is sent to the model; data is encrypted in transit; and our AI providers use it for inference only, not to train their base models.

Practitioners also asked about RBAC and access controls for SW1. As SW1 becomes more capable, our intention is to run SW1 in the context of the user interacting with it, meaning it won’t give a user access to things the user doesn’t already have access to. When SW1 can take action, we’ll provide a human-in-the-loop component that ensures the agent asks permission to take action on the user’s behalf. It’s clear from the feedback that trust and transparency are just as important as speed and automation.

If an AI agent is going to sit in the middle of every incident, it has to be accountable to the same standards as the humans it’s supporting.

 AI Agents in IT Ops: Your Key Questions, Answered.

Conclusion

Practitioners have been very clear about what they want from an AI agent in IT operations. They don’t need another chatbot or generic “AI-powered dashboard.” They need a teammate that:

• Produces clear, context-rich issue summaries to align responders fast.
• Uses root cause assist to turn raw telemetry into focused, testable hypotheses.
• Provides guided troubleshooting that helps teams move confidently from signal to action—without losing control or visibility.

That is the bar we’re holding SW1 to across the SolarWinds Platform: an AI agent built around how practitioners actually work, in the tools they already use, with the safety, control, and transparency they require.

Watch SolarWinds Day on demand

Want to see the live poll results, practitioner questions, and demos behind this blog? Watch the on-demand SolarWinds Day: Your Path to Autonomous Operational Resilience webinar to see how SW1 and SolarWinds AI bring these AI agent capabilities to life.