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By Lightrun Team
Observability platforms collect pre-instrumented logs, metrics, and distributed traces to monitor production systems and surface failures to human engineers. The adoption of AI into engineering has led observability providers to offer those same signals to agents. This is often packaged as AI observability, but the signals themselves were designed around a human investigation loop. AI coding agents work faster, consume data differently, and need feedback as they work rather than after deployment.
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By Lightrun Team
Runtime PR review means validating a code change against live variable state, real execution paths, and downstream service behavior before the merge decision. Not after a checkout regression exposes what the diff missed. As AI coding agents ship PRs faster than any reviewer can mentally simulate execution, static analysis and CI leave a structural gap that only runtime evidence can close. This article explains what that gap looks like, why it recurs, and how to close it with runtime context code review.
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By Lightrun Team
CI/CD pipelines do four things: it builds code, runs tests against mocked dependencies, lints for style violations, and scans for known vulnerability patterns. What it cannot do is validate how that code behaves under real users, real service responses, and real runtime constraints that staging was never configured to reproduce. That entire class of failure clears every gate cleanly and surfaces only in production.
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By Lightrun Team
Datadog Kubernetes monitoring tells an SRE team what failed, which pod failed, and when. It does so within seconds of the alert firing. The investigation then stalls at the same point every time: nothing in the dashboard layer can prove why a specific request behaved the way it did inside a running JVM at the moment of failure. Variable values, feature flag evaluations, and code branches are never captured.
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By Lightrun Team
Agentic workflows are reshaping how engineering teams operate, fetching context, synthesizing decisions, and shipping results across systems without human intervention. But the same design that makes them powerful adds risk in production. Agents do not crash when they hit bad data; they synthesize around it, substituting a stale value, an empty page, or a missing field for the result they were supposed to capture.
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By Gidi Freud
Today we’re launching Lightrun AI Skills, structured, repeatable investigation workflows built for AI coding agents. With Lightrun MCP, agents like Claude Code, Codex, and Cursor can already instrument live production services and reason over live runtime evidence without a redeployment. But AI agents remain non-deterministic by design, using the same tool differently every session.
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By Lightrun Team
Alert fatigue solutions have never been better, but on-call engineers are still burning out. Threshold tuning, AI triage, and alert correlation reduce the noise, but every alert that clears filtering lands with the same incomplete telemetry and triggers the same manual investigation cycle. This post explains why the evidence gap survives every fix, and how runtime context changes that.
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By Lightrun Team
Modern distributed systems fail in ways that can bypass even well-designed isolation patterns. When a failure is actively propagating across services at four in the morning, the question shifts from “how do we limit the blast radius” to “how do we confirm what it actually is.” Monitoring shows which services are in the impact zone, but it cannot show what code path caused the failure to spread, or whether it has stopped.
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By Lightrun Team
There’s a new question teams are asking. How can we prevent AI agents from deleting production. When Cursor deleted PocketOS’s entire production database in nine seconds, the agent wasn’t malfunctioning. It had full technical capability, but it was inferring operational authority from static code rather than live environment state. That gap between capability and context is the root cause. This article breaks down exactly how that happens, and what runtime visibility does to stop it.
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By Lightrun Team
Monitoring coverage, anomaly detection, and SLO-based alerting have significantly narrowed detection windows for most failure types, but MTTD remains stubbornly high for a specific silent failure. This blog covers why type mismatches, swallowed exceptions, and values that pass validation without occurring without triggering errors, and what changes when your monitoring stack can generate those signals without waiting for a failure to surface them.
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By Lightrun
Lightrun's Gidi Freud demonstrates how your AI coding agent can now investigate and fix production errors, autonomously. Watch how Cursor, guided by Lightrun's Error Remediation skill, picks up a Sentry error, instruments the live service with a runtime snapshot, captures real evidence, and opens a validated PR for approval.
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By Lightrun
In this video, Lightrun’s Dan Putman demonstrates what happens when Lightrun MCP is integrated within Claude Code. See how, once activated, Claude can ask specific questions about what services it can see and instrument in order to perform a deep investigation in production to get to a validated root cause analysis without the friction of redeploying or switching contexts.
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By Lightrun
Lightrun’s Dan Putman demonstrates the power of the latest Lightrun MCP skill. Watch how your AI code agent can now debug live applications directly in production. By connecting OpenAI's Codex to real-time runtime data via the Lightrun MCP, engineers can now generate and validate hypotheses using live telemetry and snapshots, without breaking flow. Ready to bring runtime context to your AI agents?
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By Lightrun
In this video, Dan Putman, Solution Architect at Lightrun, walks you through the power of Lightrun AI SRE. He shows how it transforms automated incident response and platform reliability by correlating signals from Monitoring tools and Incident management systems with live runtime code execution to identify and verify root causes in real time.
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By Lightrun
In this video, Lightrun's Moshe Sambol walks you through the power of Lightrun MCP and Runtime Context. A game-changer for AI-assisted development. This integration lets developers debug live issues, inspect real-world variables, and verify fixes across environments, all without leaving the IDE. With Lightrun MCP, you can: Capture live transaction state directly from Staging and Production. Identify root causes using real runtime values, not just static code. Verify fixes instantly without redeploying or context switching.
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By Lightrun
Lightrun R&D Team Lead Or Galon and Engineer Roy Chen demo how you the new Lightrun MCP allows AI coding assistants to access Runtime Context, and validate how software will behave in production.
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By Lightrun
Intermittent production bugs are hard to debug and rarely reproduce locally. Teams fall into a loop of adding logs, and every rollback slows them down. In this demo, R&D team leads Maor Yaffe and Or Golan show how an AI assistant can verify production issues using real runtime data, without redeploying. By connecting Cursor to Lightrun MCP, the agent inspects live production behavior, collects real variable values, and confirms the root cause with evidence instead of assumptions.
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By Lightrun
We’re entering a new era of AI-accelerated software development. Teams that successfully integrate AI coding assistants into their daily workflows are already seeing significant productivity gains, while those that don’t risk falling behind.
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By Lightrun
Introducing Runtime Context for AI agents The next evolution in autonomous software development. The Lightrun MCP connects IDEs and AI assistants to real runtime data, giving agents and developers the context they need to write, validate, and debug code with confidence. With Runtime Context, AI agents can: Reliable, AI-accelerated engineering starts here.
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By Lightrun
This datasheet details various specifications and requirements for installing and running Lightrun in production.
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By Lightrun
As experienced cybersecurity engineers with strong cloud and SaaS backgrounds, the Lightrun team fully recognizes the importance of embedding security as part of the product design and delivery. This document provides a high-level overview of Lightrun's security model, architecture and primary controls. While there are no 100% bulletproof solutions, the Lightrun platform is designed with a significant investment in security from the ground up, as outlined in this document.
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Lightrun is a Developer Native Observability Platform, enabling developers to securely add logs, performance metrics and traces to production and staging in real time, on demand.
Insert logs and metrics in real time even while the service is running. Debug monolith microservices, Kubernetes, Docker Swarm, ECS, Big Data workers, serverless, and more.
Developer-Native Observability Platform:
- Increase developer productivity: Spend less time debugging and more time coding. No more restarting, redeploying and reproducing when debugging.
- Enhance site reliability: Reduce MTTR and increase customer satisfaction. Identify and resolve bugs faster with less downtime.
- Resolve bugs faster: Add logs, snapshots, and metrics dynamically to your live app. Skip the traditional CI/CD pipelines.
- Debug in production, staging, anywhere: Lightrun does not interrupt running apps. Debug in any environment: production, staging, testing, dev, etc.
Save your valuable debugging time and keep your service reliable.