Resolve AI Review 2025: Can Autonomous AI Really Handle IT Incidents?

Hey, it’s Ari Vale here—AI researcher and strategist at AI Insider Labs.

Let me get straight to the point: Resolve AI isn’t just another chatbot-in-IT-clothing. It’s an autonomous, agentic AI system that aims to take repetitive incident management entirely off your on-call plate. No hype—just a hard look at whether it can actually deliver.

I’ve spent the last month embedded in engineering Slack channels, DevOps war rooms, and even red-eye Incident Review calls digging through real-world usage of Resolve AI. The big question: can this newcomer in AI IT support actually handle complex issues without melting down or turning into another dashboard you forget to check?

Spoiler alert: it’s a game-changer—but with caveats.

What Is Resolve AI?

Launched in April 2025, Resolve AI is an autonomous AI platform built to reduce Mean Time To Resolve (MTTR) in complex production environments—without exhausting your engineers.

Think of it as an always-on, always-aware AI engineer that understands your code, your stack, and your team’s workflows. And then? It acts. Not suggests. Acts.

Resolve AI was built with a singular focus: fixing operational bottlenecks without creating new ones. And for any tech leader scaling modern infra, you’ll recognize how rare that actually is.

🧠 Under the hood, it uses agentic AI—meaning, it not only understands problems contextually, but it can actually make decisions autonomously based on your system’s telemetry, logs, and historical incidents.

It integrates intelligently with AWS, Kubernetes, GitHub, Slack, and other tools your team already uses. No disruption. No retraining.

Why Resolve AI Matters in 2025

Let’s lay out the problem:

• DevOps engineers are flooded with hundreds of alerts daily
• On-call rotations are burning people out
• Fixing one incident can take hours—sometimes days—depending on system complexity
• Documentation is outdated the moment it’s written

Most solutions patch one of those symptoms. Resolve AI goes after the disease at the foundation.

It works across environments even if no one writes the SOPs. It learns by observing real behavior—not by hand-fed training data.

Still think it’s hype? Let me show you what it actually does.

Key Features That Actually Matter

1. 🔄 True Agentic Automation

We’ve seen AI bots propose fixes before. What makes Resolve AI different is that it executes fixes across cloud environments, containers, and even codebases. It builds a live knowledge graph of your systems—meaning no stale topology maps or config file archaeology.

When an alert hits, this AI doesn’t ask questions. It starts executing known patterns, pulls logs, triggers PagerDuty if needed, and scales services if thresholds are breached.

2. 🔌 Intelligent Tool Use

I was skeptical of this one. But yes—Resolve AI can navigate AWS, Kubernetes, GitHub, and internal deployment tools like a trained engineer. It doesn’t just hit APIs; it adapts to your naming conventions, labels, and tagging schemas.

And if it can’t fix something, it summarizes the problem, tags team members, and includes logs + suggested resolutions—all inside Slack.

No bouncing between dashboards. One thread. Actionable fix. Done.

3. 🧠 Custom Knowledge Base Access

Resolve can also tap into your internal runbooks, version control history, and previous incident reports. It uses this info to map likely root causes and do smart diff analysis after deploys.

It’s multilingual too. Whether your junior dev uses English, Japanese, or Portuguese—doesn’t matter. It translates prompts and feedback fluently.

4. 💻 Enhanced Remote Support

If your IT support team handles ticket after ticket with screen shares and endless “Did you try restarting it?” loops—this is a huge unlock.

Resolve AI can run diagnostics, suggest fixes, and even execute changes all during a remote session.

It tackles endpoint issues, software deployments, malfunctioning devices, and config errors on the fly.

Real-World Impact: Numbers Speak Louder Than Code

From limited public case studies shared by Resolve source, early adopters observed:

• 💥 Up to 62% reduction in MTTR (Mean Time to Resolve)
• 🔧 80% of recurring production alerts handled without human review
• 🧑‍💻 40% drop in on-call escalations
• 😌 Lower burnout metrics from engineering surveys

This isn’t a theoretical benefit—it’s operational leverage.

Who Should (and Shouldn’t) Use Resolve AI?

Great Fit If:

• You’re running microservices or hybrid cloud environments
• Your stack includes dockerized apps + monitoring tools like Datadog
• You already experience alert fatigue or MTTR > 30 minutes
• Your IT or dev team has more tickets than time

Maybe Skip For Now If:

• You’re a solo engineer running a small SaaS
• You don’t have high uptime demands or 24/7 systems
• You already use full-scale outsourced DevOps or NOC

That said, Resolve is designed to grow with your environment. Start small—connect to your CI/CD and cloud infra—and scale automation forward.

Pricing

Resolve AI hasn’t published public pricing as of now. From what we know, pricing is usage- or scale-based and likely requires an initial consultation.

Honestly? That’s normal for tooling that’s deeply integrated. If MTTR reduction alone saves thousands per month in breach costs or engineer effort, the cost usually pays for itself before the second on-call cycle.

Want clarity? Use my affiliate link below and request a demo call through their form:

How Does It Compare to Other “AI Ops” Tools?

Most “AI IT” tools are glorified analytics dashboards. Some, like LogMeIn’s AI offering or Resolve RCMS, either aren’t truly autonomous or are limited to help desk environments.

Resolve AI is in a league of its own for teams maintaining infrastructure, not just support ticket flow.

And here’s the big one: it’s not just AI that thinks—it reacts autonomously.

This matters because passive insights don’t save systems. Actions do.

My Personal Take as an AI Systems Analyst

I’ve seen tools promise “automated resolution” before—and fall flat. They usually trip over context, can’t understand custom stacks, and end up being expensive toys.

Resolve AI passed my stress test.

It navigates unknowns, rolls with changing environments, and never asked me to pre-load some bloated YAML file of company knowledge.

Instead, it learned by observing. Then acted. That’s viable AI architecture at work—and a powerful example of where this tech is heading.

The one caution? Enterprise AI is still fragile. I wouldn’t hand over 100% of incident control yet. Start with controlled alerts. Then expand.

That said, at this trajectory? In a year, Resolve AI could very well become your most reliable engineer.

Final Verdict: Should You Try Resolve AI?

• ✅ Yes, if you’re tired of alert overload and burnout
• ✅ Yes, if you want tangible automation, not dashboards
• ✅ Yes, if reducing MTTR impacts your bottom line
• ❌ Skip if you’re a lightweight stack without real incident noise

Resolve AI is pushing the edge of what agentic, environment-aware AI can do in real-world IT ops.

If you want more uptime and less midnight debugging, this is where you aim your 2025 IT budget.

Explore the next evolution of IT support and incident automation. I’ll keep tracking where it goes from here. If you deploy Resolve AI, I’d love to hear how it performs in your stack—email me or connect on LinkedIn.

Until then, keep building, keep scaling—smarter.

— Ari Vale
AI Systems Analyst
AI Insider Labs