Lightrun unveils AI SRE tool for live runtime debugging

Lightrun has announced an AI-driven site reliability engineering product that uses live, in-line runtime context to investigate and remediate software issues in running systems.

The company positions the product as an "AI SRE" that can generate missing runtime evidence on demand and validate hypotheses against live execution data. Lightrun says this reduces the need for redeployments and rollbacks during investigations and fix validation.

Engineering teams have increased their use of AI coding tools and agents over the past two years. Lightrun argues this has increased the volume of code changes and shifted more effort toward verification and runtime troubleshooting, where behaviour can be difficult to reproduce.

Many tools marketed as AI SRE focus on post-incident analysis and depend on logs, traces and metrics that were already captured. That model breaks down when telemetry is missing or incomplete, leading teams into extended cycles of manual debugging and repeated deployments.

Live runtime context

Lightrun says its AI SRE differs by interacting with live systems and gathering code-level evidence while software runs. The product is built on what it calls a Runtime Context engine and uses a patented Sandbox designed for safe interaction with production environments.

Lightrun describes the workflow as dynamic instrumentation applied to running services. It generates additional context during incidents, or whenever engineers need more detail than existing telemetry provides. The aim is to validate root cause against "ground truth" from live execution.

The product is intended to support reliability work across the software development lifecycle, including detection during development and testing and investigation during live incidents. It is designed for teams responsible for the behaviour and outcomes of production software.

Jim Mercer, Program Vice President, Software Development, DevOps, and DevSecOps at IDC, said Lightrun is targeting a persistent gap in incident response workflows.

"Lightrun addresses a structural visibility gap in the emerging AI site reliability engineering workflows (SRE) market," said Jim Mercer, Program Vice President, Software Development, DevOps, and DevSecOps at IDC. "By integrating dynamic instrumentation into SRE workflows, the company enables validation of root cause and remediation against live execution, reducing reliance on static, pre-instrumented telemetry and strengthening reliability across the software development lifecycle."

Operational claims

Lightrun lists several outcomes it associates with the product, including root cause analysis based on newly generated evidence from live environments, runtime-validated code changes, and remote debugging sessions that inspect execution behaviour.

It also says it can add telemetry to running systems when traditional observability tools have visibility gaps. Another claimed outcome is less reliance on "war rooms," through autonomous remediation that produces a code fix before escalation to a human.

Lightrun also frames the product as a way to handle "unknown unknowns" introduced by multiple AI agents across the software development lifecycle. It argues that as teams adopt more automated code generation and operational tooling, incident patterns can change and require new forms of evidence gathering.

AT&T AVP Engineering Zahi Kapeluto linked the problem to the limits of conventional telemetry when signals are not connected to execution context.

"Modern, AI-driven software reliability depends on connecting telemetry to real execution context. Without understanding how code behaves in live environments, alerts and metrics alone don't tell the full story. Lightrun helps our teams close that gap by exposing runtime behavior directly, enabling faster investigation and more confident remediation," said Kapeluto.

Market positioning

Lightrun says it has been recognised in the 2026 Gartner Market Guide for AI Site Reliability Engineering Tooling. It also says demand is emerging for AI SRE products as organisations invest in AI-driven reliability and autonomous operations.

The announcement reinforces an approach that sits between observability tooling and incident-response automation. Rather than relying on pre-defined instrumentation and static telemetry, it focuses on gathering evidence when engineers or agents need more detail, using access to live execution paths.

Chief Executive Ilan Peleg said visibility is the limiting factor for automated remediation.

"AI cannot resolve what it cannot see. Lightrun's runtime context engine allows AI to see application behavior at a single line level of granularity, which positions us to streamline remediation for any software issues in real-time," said Peleg. "Trusted by Fortune 100 companies and the largest enterprises in the world, Lightrun is proud to lead the way in making self-healing software a reality."

Lightrun says it has raised USD $110 million in funding from investors including Accel and Insight Partners, and counts large enterprises among its customers, including AT&T, Citi, Microsoft, Salesforce and SAP.