The only AI SRE that
creates runtime evidence
Lightrun AI SRE delivers faster, more accurate triage, root-cause analysis, fix suggestion,
and change validation, all without a single redeployment.
Trusted by engineering teams at Fortune 500s
Trust your AI SRE assistant
Lightrun AI SRE is an autonomous product built for SRE, DevOps, and engineering teams.
It combines AI reasoning and live runtime context to resolve incidents fast.
Every decision is
based on evidence
Each AI diagnosis and fix suggestion is validated with runtime proof, rather than probability-based inferences.
Issues are resolved
fast and accurately
Cut MTTR without risking increased blast radius by getting clear explanations of behavior, and confirmation of what remediation is safe.
Engineers stay focused
on high-impact work
Reduce engineer toil and reproductions with real-time AI-led investigations, all without removing human control.
Surgical, real-time site reliability engineering
Every diagnosis and fix proposal is evidence-based and verified against live runtime behavior.
Understand complex system architecture
Map shifting microservices, complex dependencies and runtime behaviors dynamically. End dependency on outdated docs and static diagrams.
Triage emerging issues before they cause incidents
Detect production errors and performance degradations as they arise. Correlate these service-level issues with execution evidence to prioritize incidents.
Prove root causes with live runtime evidence
Lightrun AI SRE instruments running applications at the failure point to fill data gaps left by static telemetry. The AI’s reasoning is evidence, not probability-based.
Validate fix proposals against remote environments
Lightrun uses the defined root causes to propose fixes that consider full system architecture. Every proposal is shared with a verifiable chain of thought to ensure trust.
Generate postmortems to improve future incident resolution
Lightrun writes a postmortem for each event. It details the timeline, root cause, and follow-ups and the successful resolution strategy to learn and improve.
How does Lightrun accelerate incident resolution across the entire lifecycle?
From detection to post-mortem, Lightrun AI SRE gives real-time production insight at every stage of incident resolution.
Detection & Intake
Support Tier 1, Monitoring Systems, Customer Success Validate the problem and classify impact
- What is the customer experiencing?
- Is this reproducible?
- What is the impact — users, region, tenant?
- When did the issue start?
- Is this a known issue?
- Does this affect SLAs or strategic accounts?
- Did error rate or latency exceed SLO thresholds?
- Capture additional telemetry on demand
- Enrich incident context with live production data
- Reduce time to actionable signal before escalation
Triage & Assignment
Support Tier 2, SRE On call, Incident Manager Confirm severity and route to correct team
- Which subsystem is failing?
- Can logs give a quick clue?
- Is this similar to previous incidents?
- Which services are involved?
- Is production healthy overall?
- Is rollback needed immediately?
- Infrastructure or application issue?
- Is severity correct?
- Which team should own this?
- Do we need a bridge call?
- Inspect live services without redeploying
- Identify failing services and code paths immediately
- Support rollback planning with real time runtime insight
Containment & Immediate Mitigation
SRE, Dev On call, Incident Commander Stop customer impact quickly
- Should we fail over or scale?
- Is config change safe?
- Disable faulty feature flag?
- Which code paths are involved?
- Caused by recent deployment?
- Can we hotfix or revert safely?
- Fastest reversible action?
- ETA for mitigation?
- Inspect live code paths safely
- Validate deployment regressions
- Verify mitigation effectiveness immediately
Root Cause Investigation
Dev Team, SRE, QA, Incident Manager Identify precise fault
- Which commit introduced regression?
- Logs trace to specific module?
- Can we replicate in staging?
- Correlated with infrastructure instability?
- Config drift or resource constraints?
- Why did tests not catch this?
- Can we confirm root cause?
- Identify exact code pathways
- Narrow root cause to line level
- Capture dynamic logs without redeploy
Permanent Fix & Validation
Dev Team, QA, Release Engineering Deliver long term fix
- Minimal safe change?
- Need refactoring or guardrails?
- Any regressions?
- Need new tests?
- Safe to deploy now?
- Validate fix in real runtime scenarios
- Confirm assumptions before rollout
- Reduce guesswork in refactoring
Deployment & Monitoring
SRE, Dev, Release Engineering Release fix and ensure stability
- Is error rate decreasing?
- Any abnormal metrics?
- Functionality behaving normally?
- Can we close incident?
- Inject temporary telemetry to validate stability
- Confirm fix success dynamically
- Remove instrumentation once stable
Post Incident Review
Dev Lead, SRE Lead, Product Manager, Incident Manager Prevent recurrence
- Why was bug introduced?
- Process gaps?
- Were alerts sufficient?
- Missing requirements or feature risks?
- What action items?
- Who owns each task?
- Provide runtime evidence for post mortem
- Identify telemetry gaps
- Convert insights into preventive guardrails
AI SRE grounded in runtime truth
Live system and verified execution data power more accurate AI SRE.
Security and privacy
Securely supporting the largest companies in the world across regulated industries
Enterprise Compliance
ISO 27001 and SOC 2 Type II certified with GDPR and HIPAA alignment. Full RBAC, SSO, and audit logging.
Lightrun Sandbox
Read-only execution with instrumentation isolation, without impact on production.
End-to-End Encryption
TLS 1.3 in transit and AES-256 encryption at rest, backed by AWS KMS with annual key rotation.
Secure AI SRE Integrations
Read-only integrations with least-privilege access. Customer data is never modified.
Data Privacy Controls
Configurable retention, PII redaction, prompt sanitization, and zero data retention with AI providers.
IP & AI Protection
No source code storage, no model training on customer data, and strict execution guardrails.
Tenant Isolation
Logical tenant separation, dedicated secret storage & fully isolated AI sandboxes.
Taboola reclaimed 260+ hours of monthly engineering
capacity by eliminating manual reproduction
AT&T reduced Time to Resolve incidents from
5 hours to 30 minutes avoiding costly war rooms
“When it comes to priority-one tickets, customers can’t wait days for a fix. Lightrun helps us reduce that to hours, that’s a huge win for us and for our customers.”
Hood Munaim SVP, Head of Product Engineering
Priceline increased developer productivity
by 30% across workflows over 2000+ services
“Lightrun not only saved us days, if not weeks, of painstaking debugging but provided an efficient approach to tackling complex issues in production.” Tomer Glicksman, SalesForce
Drata accelerated incident response velocity by 30% while maintaining strict compliance standards.
Inditex engineers used Lightrun’s live, dynamic logs and snapshots directly from their IDE to dig into a critical production issue and uncover a rounding bug quickly.
“The unique solutions that Lightrun is developing dramatically impact how developers operate.”
Siris Singh, Global Head of Markets Strategic Investments
Works with your tool stack
100+ integrations, and native agents for JVM, Node.js, Python, and Go connect directly to your IDEs, pipelines, and cloud environments.
Frequently asked questions
about Lightrun AI SRE
An AI SRE is an autonomous system that manages the full reliability lifecycle, detection, triage, root cause analysis, fix validation, and postmortems, without constant human input. Unlike copilots or static automation, it can act independently, learn, and adapt to new failure scenarios.
While most AI SRE tools can only reason over existing data and telemetry, Lightrun AI SRE is able to safely generate runtime data from live running application on demand. This allows the AI to prove a root cause and validate fixes, without relying on probability and inference.
Lightrun integrates into existing incident response workflows by serving as a real-time investigation layer. When an alert is triggered in a tool like PagerDuty or an APM, Lightrun AI SRE can automatically surface a live snapshot of the error directly within a dedicated Slack incident channel. This integration eliminates the need for context switching, allowing engineering teams to view live runtime evidence and variable states within their primary communication platform.
Yes, Lightrun AI SRE includes specialized workflows designed to evaluate the blast radius and business severity of an ongoing incident. By querying infrastructure and telemetry connectors, the AI identifies which specific services are degraded and which user segments are being affected in real-time. This automated assessment allows on-call engineers to prioritize their response efforts based on the actual scale of the impact rather than estimated severity levels.
The AI SRE assistant operates as an interactive member of your communication stack by providing natural language investigation results directly within Slack. When an incident is detected or a question is asked via the Slack interface, the AI performs the necessary backend queries across your connected tools and posts its findings, evidence, and suggested next steps into the channel. This creates a shared source of truth for the entire response team and allows multiple engineers to collaborate on the AI’s output without switching between different dashboard environments.
Lightrun AI SRE functions as a centralized orchestration layer that integrates natively with code repositories, telemetry providers, and communication platforms through a series of pre-built connectors. By linking directly to systems like GitHub for code context, Datadog or Prometheus for metrics, and Slack for collaboration, the AI can cross-reference real-time system behavior with recent code changes. This unified access allows the assistant to query multiple siloed systems simultaneously to build a comprehensive evidence chain without requiring manual data extraction from the user.