Operations | Monitoring | ITSM | DevOps | Cloud

Kiro Can Now Reason With Lightrun's Live Runtime Context

AI code generation is fast. Making it reliable requires runtime context. Today, Kiro gains live runtime visibility with the Lightrun MCP. This grounds AI-assisted development in how code actually behaves at runtime. Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It moves from specification to production with speed and structure, helping teams turn intent into working code. But until now, like every AI coding assistant, Kiro had a major blind spot.

Lightrun Runtime Context MCP | 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.

What is Runtime Context? A Practical Definition for the AI Era

TLDR: Runtime Context is live, execution-level access to a running production system. It lets engineers and AI agents ask precise questions of running code and get answers immediately, without redeploying or interrupting users. This is the new baseline for reliability.

Lightrun MCP: Your AI Assistant Now Debugs and Validates Production Code

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.

How to Ensure AI-Generated Code is Reliable with Runtime Context

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.

Lightrun 'Runtime Context' Empowers AI Coding Agents to Build Software That Works in the Real World

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.

Runtime Context for AI Agents with Lightrun MCP

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.

Side-by-Side Variable Comparison for Snapshot Debugging

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.