Operations | Monitoring | ITSM | DevOps | Cloud

TLDR: AI coding assistants have sped up code delivery, but created a validation gap. Historic telemetry and static analysis cannot predict the behavior of unfamiliar, high-volume code. Lightrun’s Runtime Context MCP closes that gap, allowing AI assistants to verify behavior before it breaks, and resolve issues in real time.

Safe, Direct Access to Runtime Code Across Staging, Pre-prod and Production via MCP Enables Fundamental Step Forward in Autonomous Software Delivery and Reliability for Enterprises NEW YORK, December 10, 2025 – Lightrun, a leader in software reliability, today launched its new Model Context Protocol (MCP) solution, enabling the industry’s first fully integrated Runtime Context for AI coding agents.

When you’re debugging a tricky issue in a distributed system, “what changed?” is often the most important question. You add logs, you capture data, you redeploy, and suddenly your browser is full of open tabs, copied JSON blobs, and screenshots of log lines. Comparing behavior between two requests, two users, or two releases turns into a manual, error-prone chore. Lightrun Snapshots were built to fix the data collection side of that story.

Every hour developers spend troubleshooting is an hour they’re not building features, innovating, or delivering value to customers. Yet in most organizations, issue management and debugging remains one of the biggest drains on productivity and release velocity. That frustration is exactly what led our founders, themselves developers, to create Lightrun.

(March 18, 2025) — Lightrun is proud to have been named to Fast Company’s prestigious list of the World’s Most Innovative Companies of 2025. This year’s list shines a spotlight on businesses that are shaping industry and culture through their innovations to set new standards and achieve remarkable milestones in all sectors of the economy. Alongside the World’s 50 Most Innovative Companies, Fast Company recognizes 609 organizations across 58 sectors and regions.

Debugging software, whether during development or incident investigation, often begins with a manual and error-prone process. Developers typically scatter logs and snapshots across the codebase, allowing them to trigger multiple times. They then inspect the outputs and sift through the results to identify those relevant to the issue under investigation. Developers tend to group results that stem from the same user request or transaction.

In the fast-paced world of software development, debugging remains one of the most time-consuming and complex tasks for engineers. Modern observability tools that use logs, metrics, and traces help developers gain insights into system behavior, but they still require manual effort to identify and fix issues.

In today’s fast-paced software landscape, downtime is costly, debugging is time-consuming, and developers are constantly under pressure to resolve issues quickly. Observability tools have traditionally been built for operations and SRE teams, focusing on post-mortem analysis rather than proactive debugging. When developers gain real-time insights into live applications and fix issues without disrupting the software lifecycle it has been proven to be a game changer for a myriad of reasons.