This third article in our Agentic AI Essentials series lays out a process for capturing feedback and setting out a clear framework for effective use of an agentic system. It’s more than just collecting data; it's building a system you can trust. Let’s explore what success looks like in your environment.

Explicit Feedback vs. Implicit Feedback

At its core, measuring ROI is figuring out whether the agent is truly helping. That starts with feedback, but not all feedback is the same. There are two main types:

  • Explicit feedback: This is the direct input you ask for, like thumbs up, thumbs down, or scaled ratings.
  • Implicit feedback: This comes from what happens after the agent acts. Was a ticket resolved and left closed? Did the alert resolution hold steady? Or did someone have to undo what the agent did?

Both kinds of feedback matter, but implicit feedback is often the more powerful signal because actions have visible consequences. Consider incident management. If an agent resolves a ticket and no one reopens it, that’s a clear positive outcome. If the same ticket is reopened, that’s a signal that the resolution fell short of expectations. The same logic applies in alerting: a successful resolution is a win; a rollback is a red flag.

Explicit ratings, on the other hand, can get tricky. Asking for a one-to-five score often produces inconsistent or overly subjective responses. In practice, a simple thumbs up or down is underrated—it’s fast, clear, and unambiguous. When IT teams combine this with implicit signals from real-world outcomes, they get a much fuller picture of whether the agent is doing its job.

An Evolving Criteria for Measurement

What counts as “effective” varies by use case, so every organization has to define its own criteria for success. If the agent is working in a chat interface, a thumbs-up or down might be enough. But if it’s performing an action, like creating a dashboard or running a remediation workflow, you’ll need a more outcome-driven measure. Offline evaluation is one reliable way to do this. Set up a known scenario—say, simulating a database outage that blocks an “add to cart” function—and then let the agent attempt a fix. Since you already know the right solution, you can compare the agent’s steps to the gold standard and see where they align or fall short. The result might be a confidence score (90% accuracy, for example), which gives you a benchmark for whether to trust it in production.

Of course, these criteria evolve. Teams might start with a few assumptions about what to measure, only to discover in practice that different signals matter more. That’s not a flaw, it’s a natural part of the process. The key is to treat measurement as iterative. You begin with a framework, refine it based on actual usage, and continue building a system of trust over time. And here’s the paradox: you often don’t know what feedback will matter most until you put the agent in people’s hands. The good news is that you can always adapt. Whether it’s tweaking metrics, adding new indicators, or adjusting the way you capture outcomes, you can evolve your measures in tandem with the system itself.

Exploring the Spectrum of Autonomy

Another factor in ROI is the level of autonomy you allow the system. Initially, agents typically operate under close human supervision. This ensures accountability and provides a safety net. But too much oversight can become a bottleneck, slowing down the very efficiencies the AI is meant to deliver. Over time, as confidence builds, teams may choose to move more tasks toward full automation. For example, you might trust the agent to automatically resolve low-stakes alerts—saving human attention for the handful of issues that really need it. But in high-stakes scenarios, such as financial transactions or critical database updates, human approval remains essential.

The balance lies in treating autonomy as a spectrum:

  • On one end are routine, low-risk tasks where you can safely let the agent act independently
  • On the other end are high-risk, high-impact actions where a human must stay in the loop

As the system proves itself, more work shifts toward automation, reducing cognitive load and freeing teams to focus on higher-value decisions. The principle extends beyond IT. In healthcare, for instance, doctors might happily let AI capture symptoms and generate initial reports, but they’d never hand over full responsibility for a complex surgery. The same balance applies in IT operations: let the system handle what it can, while keeping human judgment where it matters most.

Define What Demonstrates Success

For organizations ready to implement agentic AI, the process works best when it’s deliberate and staged. The first step is clarity: know exactly what you want the agent to do. From there, define indicators that will tell you whether it’s succeeding. Measuring ROI involves examining the entire decision-making process. In most cases, an agent will:

  1. Access and analyze data
  2. Make a decision based on that analysis
  3. Act on that decision

Your evaluation should account for all three. Offline testing with golden datasets is one effective way to do this. By running the agent against known scenarios, you can see whether its decisions align with the outcomes you’d expect. Once the system is in production, measurement doesn’t stop. Capture every decision, action, and outcome, and periodically run offline evaluations to ensure performance holds up. This iterative process may feel methodical, but it’s what builds confidence, both in the technology and in the teams using it.

Trust Is the Real ROI

Successful adoption of agentic AI isn’t just about turning it on and walking away. It’s about building a feedback loop that evolves, just like the systems themselves. That loop—explicit and implicit feedback, evolving criteria, balanced autonomy, and deliberate evaluation—becomes the foundation for the real ROI: trust. Trust is what lets organizations move beyond experimentation and toward widespread adoption, where AI isn’t just assisting but actively strengthening operations. The path forward is not fixed. The tools we use will evolve, the metrics will change, and the role of human oversight will continue to adapt. However, with a strong feedback system in place, you can be confident that your agent is providing value, learning from each action, and helping your team to spend less time dealing with issues and more time driving the business forward.

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.

© 2026 SolarWinds Worldwide, LLC. All rights reserved.

Tags:
agentic ai
agentic ai series
roi