New york, Dec 11: 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. This new capability is a step change in autonomous code writing that gives tools like Cursor and GitHub Copilot full visibility into how code behaves after deployment, filling a missing piece of the AI development ecosystem for enterprises.
AI assistants can generate code rapidly, but studies from Stanford and Google have shown that it fails at high rates once exposed to real-world traffic, dependencies, and workloads. Furthermore, once the code leaves the Integrated Development Environment (IDE), AI cannot see what takes place in staging, pre-production, or production. As a result, teams report spending up to 17 hours a week debugging and refactoring bad code and 60–70% of teams’ time spent debugging and a 41% rise in bug rates.
Lightrun’s Runtime Context directly addresses this problem by bridging the gap between the IDE, the AI assistant, and runtime, providing crucial context to the agent and the developer behind it. Developers can now ask their coding assistant to check environment traffic before writing a module, investigate a production failure, or add the instrumentation needed to validate behavior. Lightrun’s MCP acts as the secure bridge, enabling the AI agents to add logs and traces in real time, capture snapshots, investigate issues safely, and even suggest fixes, all without requiring engineers to manually reproduce issues. Runtime context enriches every AI-generated line of code with inline runtime context and observability. This extends across the SDLC and into production, helping engineers move faster while ensuring code remains reliable under real-world conditions
“AI has taken over much of the creative part of coding,” said Ilan Peleg, CEO and co-founder of Lightrun. “However, debugging across environments has remained painfully manual. With Runtime Context, AI can finally participate in the full lifecycle by writing code, validating and debugging it, and remediating issues based on real-world behavior. This is the next evolution of autonomous software development.”
The Runtime Context model enables AI agents to:
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Trigger remote debugging sessions inside staging, pre-production, or production
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Access production-grade telemetry in real time
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Propose fixes based on actual runtime behavior
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Deliver code that is reliable, stable, and deployment-ready
Lightrun customers can now expect faster debugging cycles, higher deployment reliability, and AI-generated code that better withstands real traffic and dependencies.
