From Promise to Practice: What Real AI SRE Can Actually Do When Production Breaks

We’ve written before about the advantages of training an AI SRE on real telemetry data rather than generic Kubernetes documentation. We’ve explained why RAG augmentation based on actual high-scale workload patterns produces better results than LLMs trained on generic scenarios or forum threads. The theory makes sense, the architecture is sound, and the approach is defensible.

But theory only matters if it translates to real productivity gains when production is on fire in the middle of the night.

This series demonstrates what AI SRE trained on real workloads actually looks like in practice. We’re going to walk through real troubleshooting scenarios that our customers encounter daily, showing the before and after of AI-powered investigations. Not synthetic fine tuned demos or cherry-picked success stories, but actual incidents with real metrics on time to resolution, team size required, and expertise needed.

What Trained AI Changes About Investigation

When your AI has observed thousands of production incidents across Kubernetes clusters, it doesn’t just surface error messages from logs with generic recommendations. It correlates pod events with configuration changes, maps resource exhaustion patterns to specific workload behaviors, and connects cascading failures back to their triggering events. It knows which remediation paths actually work because it’s seen the full resolution cycle, not just the initial symptoms.

An AI trained on real troubleshooting telemetry compresses that loop and parallelizes the investigation. While human engineers follow sequential troubleshooting paths (check monitoring, then logs, then configs, then events), AI simultaneously examines configuration drift, deployment timing, pod events, and historical patterns. It already knows how these elements correlate because it’s seen that pattern hundreds of times. The investigation that normally requires 3-4 engineers and 3-10 hours happens in seconds.

But of course, like all tools, and particularly AI tools – your troubleshooting will only be as good as the data quality it is trained on.

AI SRE – More Than Just ‘Root Cause Analysis’

That’s why traditional AI-driven RCA tools tell you what went wrong after the fact, and other AI SRE tools provide some suggestions that can lead to the solution – but don’t take you all the way through to remediation. 

AI SRE platforms that actually work tell you what to do about it while the incident is happening. They connect detection to investigation to remediation in a single workflow – based on incidents that have been observed and remediated before.

When a deployment breaks because someone changed a ConfigMap key, most tools will eventually surface that information if you know where to look. Komodor’s Agentic AI, Klaudia,  identifies the config change, correlates it with the deployment timing, and provides the specific rollback action needed. The entire investigation takes seconds instead of the usual cycle of examining pod events, checking recent changes, validating configurations, and coordinating with multiple teams to confirm the hypothesis.

This matters more as Kubernetes usage expands beyond application developers. Data engineers and data scientists now deploy workloads directly to Kubernetes, often without deep platform knowledge. Each new user population creates new escalation patterns and incident types. SRE teams can’t scale linearly with user growth, which means they need dramatically more efficient troubleshooting workflows – and tools that cater to their unique expertise to achieve real productivity gains and MTTR.

What Trained AI Can Actually Deliver

The scenarios we’ll cover in this series demonstrate what production-grade AI SRE looks like in practice. These aren’t contrived examples or demo environments. They’re actual incidents from Komodor customers, showing the before and after of AI-augmented SRE.

GPU hardware failures that would normally require analyzing pod YAML, examining pod events, reading logs, looking up job state, examining other pods on the same node, cordoning the node, and running GPU diagnostics. With full context and trained pattern recognition, the entire process compresses to automated RCA and guided remediation.

Failed deployments from configuration changes that trigger the standard escalation cycle of checking monitoring, inspecting logs, examining ConfigMaps, validating deployments, querying historical events, and eventually bringing in senior engineers who remember that someone changed a key name last week. Context-aware AI identifies the configuration drift immediately and maps it to the deployment failure.

Node termination events that cause partial outages require confirming the termination, identifying the node pool owner, classifying the termination type, checking for hardware or OS issues, examining affected pods, looking for mismatch in taints and tolerations, and then cordoning the problematic node while adding capacity buffers and verifying surge capacity. Multiple teams, multiple tools, multiple hours. Or one engineer with an AI assistant that has seen this pattern before.

The common thread is elimination of investigative toil. Not the kind of toil that involves repetitive tasks you can script away, but the cognitive toil of correlation, hypothesis testing, and tribal knowledge lookup. When your AI SRE platform has actually learned from thousands of real incidents, it can compress that investigative loop from hours to seconds.

What This Series Covers

Over the next several posts, we’ll walk through these specific troubleshooting scenarios in detail. Each post covers a real incident with full metrics on time, team size, and expertise required with and without AI-augmented investigation.

The goal is to show how telemetry-trained AI translates into actual velocity gains for SRE teams – and business gains for companies. Not theoretical improvements or percentage optimizations, but the practical difference between resolving an incident in minutes versus hours, between requiring senior engineer expertise versus enabling any team member to handle it.