Resolve customer issues without waiting for reproductions

Yes. Lightrun AI SRE gives support teams the runtime evidence they need to investigate and resolve many incidents independently. It correlates customer reports with live system signals, classifies whether an issue is a bug or a configuration problem, and provides verified findings. When escalation is needed, engineers receive a complete evidence package, not a vague ticket.

Yes. Lightrun uses read-only, sandboxed instrumentation with a negligible performance footprint. It observes variable values and execution paths in real time without modifying code, restarting services, or impacting users. It requires no redeployment to activate. Enterprise customers including AT&T, Taboola, and Priceline run Lightrun today.

No. Support engineers describe the customer’s issue in plain language, Lightrun AI SRE handles the technical investigation. It queries logs, correlates alerts, assesses impact, and identifies root cause automatically. When engineering involvement is needed, AI SRE produces a complete, verified evidence package so developers can act immediately rather than starting their own investigation from scratch.

Lightrun AI SRE connects with observability platforms such as Datadog, infrastructure tooling, knowledge bases, alerting systems, and communication tools including Slack, Jira, and PagerDuty. Investigations happen inside tools support and engineering teams already use, and findings attach directly to existing tickets. For a full list of supported connectors, see the AI SRE documentation.

Lightrun AI SRE correlates signals across all affected services simultaneously, identifying failing dependencies, mapping blast radius, and building a causal chain across the full incident scope. Rather than support engineers manually stitching together logs from different systems, AI SRE returns a unified view of what broke, where, and why, giving both support and engineering a shared starting point.

Observability tools surface historical data captured before anyone knew what to look for. Lightrun AI SRE instruments live production systems at the moment of an incident, capturing runtime evidence dynamically, variable values, execution paths, and system state, targeted at the specific failure. The difference is between searching logs after the fact and observing the failure as it happens.

AI coding agents are blind to what’s happening in your running system. Their reasoning depends entirely on context a human provides, which in an incident is exactly what’s missing. Lightrun AI SRE observes the live production environment directly, correlating logs, traces, metrics, and recent deployments to identify root cause from real runtime evidence, not inference from static code.