What’s It Like When AI Helps Solve Incidents the Way Engineers Do

You’ve likely been in situations where incident response doesn’t start with alarms or dashboards turning red. It started slowly, perhaps going unnoticed at first. The number of support tickets went up slightly, the engineers mentioned that certain flows seem slower than usual, or certain tools or features were lagging.

Nothing is broken, but the system doesn’t feel as healthy as it was. The database team finds that performance is mostly fine, but queries are running a bit longer than usual. The backend team checks the logs and sees that requests are being completed, but sometimes take longer than expected. The platform team looks at Kubernetes and reports that the nodes are fine, the pods are running, and maybe they’re seeing a few restarts here and there. 

This is where the real detective work begins. Say the team notices that a batch analytics job has been running more haphazardly since a recent update. It uses more CPU and memory than expected in short bursts. During those bursts, Kubernetes reschedules workloads across nodes, creating temporary resource pressure. And as a result, services slow down and then go back to normal. 

The root cause of a problem like this can take hours or days to track down. Maybe even weeks. Fixing it means tuning scheduling, right-sizing memory, maybe isolating workloads, and adjusting scaling rules. Eventually, the system settles back into balance again. But it takes many people, multiple tools, and a fair amount of mental energy to connect all the dots.

Reliability isn’t just about uptime. It’s about how quickly you can understand what’s happening, especially when the problem isn’t obvious.

Where an AI SRE Fits into this Picture

Now imagine an agentic AI SRE tackling this situation in the way real engineering teams do.

Instead of one AI assistant, there are multiple AI specialists continuously examining different layers of the stack:  Kubernetes scheduling, workload patterns, service health, database behavior, change history, and more. An orchestrator agent pulls those perspectives together, similar to the way an SRE overseeing the team would handle the investigation.Instead of simply noting that CPU usage is high, it can discover that there’s intermittent resource pressure that correlates with the slowdown in your services. What’s even better is that it can show why there’s resource pressure and where it comes from, when the problem started, and how to remediate the situation. Your engineers can review the evidence and reasoning, and apply the fix or have the AI SRE make the adjustments. In this example, the autonomous AI doesn’t replace your judgment; it helps you understand the situation in minutes or seconds. There’s another terrific example of how this works in our blog The War Room of AI Agents: Why the Future of AI SRE is Multi-Agent Orchestration.

Of course, with an autonomous agentic AI SRE like Komodor that monitors your system 24/7, you probably wouldn’t have gotten into this situation in the first place. Komodor would have noticed the slowdown immediately and autonomously applied the fix, before anyone ever noticed there was a problem.

The Multi-Agent Advantage in Action

The difference between single-agent AI and Komodor’s multi-agent system is like the difference between a tool you use and a teammate you work with. With a tool, you need to know what to ask. A teammate anticipates what you need.  A tool presents data. A teammate provides insight.

Real-world reliability work depends on collaborative thinking. So the AI should work that way too. Komodor’s AI SRE is built on Klaudia, an agentic architecture designed to mirror how expert SRE and platform  teams operate.

Klaudia works the way DevOps engineers and SREs do, detecting, investigating, remediating, and optimizing cloud- native infrastructure. It uses hundreds of specialized workflows and SME agents running continuously to identify and resolve issues, with or without a human in the loop.

The Orchestrator (The Incident Commander): This primary agent manages the investigation lifecycle, synthesizes findings from specialists, and maintains a coherent narrative. It knows who to ask and how to connect the dots.

The SME Specialists: The Klaudia orchestration layer pairs workflow agents with SME (Subject Matter Expert) agents, which are specialized components trained in complex cloud-native technologies like autoscalers, NVIDIA GPUs, Istio, ArgoCD, vLLM, and more. These domain-specific agents act as experts, contributing relevant pieces of context or performing deep dives when called upon. For example, there’s a Kubernetes Specialist that understands pod lifecycle and resource constraints. A DB Expert analyzes query performance and connection patterns. An AWS Specialist monitors cloud infrastructure and service-level events, and the Network Analyst examines traffic patterns and connectivity problems. And there are many more specialists available. You get the idea. 

What Happens Behind the Scenes

In a complex incident, different components can point in different directions. Reconciling those signals into a clear root cause isn’t trivial, even for humans. For an AI system, the challenge is the same: you need a way to weigh evidence, resolve contradictions, and keep the investigation moving forward. Guardrails and explainable reasoning are essential here. Klaudia’s orchestration layer ensures that recommendations are tied back to real system evidence and that every action stays within safe boundaries. As a result, teams can trust both the conclusions and the pace of remediation.

Real System Evidence: Klaudia is designed to “ground” its AI logic by pulling real-time data—such as Kubernetes events, logs, and metrics—rather than relying solely on large language model patterns. This is intended to eliminate “hallucinations” and ensure conclusions are based on what is actually happening in the cluster.

Safe Boundaries: The “orchestration layer” acts as a governance mechanism. It is built to ensure that any automated or suggested remediation action complies with predefined infrastructure policies and safety checks before execution.

Trust in Remediation: By providing a clear audit trail of evidence for every conclusion, the system aims to give human operators the confidence to let the AI handle incident response at a faster pace than manual troubleshooting would allow.

How This Directly Improves Reliability

Faster and more accurate incident resolution is the foundation of system reliability.  

That middle-of-the-night alert won’t disappear, but it will change fundamentally thanks to Klaudia. Instead of assembling five frantic engineers trying to piece together what happened while the system continues to degrade, you start from a structured investigation that has already correlated signals and proposed safe next steps. 

More importantly, the AI SRE works 24/7 to detect problems early and learn from patterns to prevent issues before they escalate. This is the shift from reactive incident response to proactive reliability engineering.

In the next post, we’ll go a little deeper into how that actually works in practice. We’ll explain how multiple AI specialists cooperate during an incident, how decisions are made, and how safety and trust stay built into the loop the entire time.