Lightrun has introduced a Model Context Protocol solution that connects AI coding assistants directly with live software environments, in an effort to reduce failures when AI-written code reaches staging and production.
The company calls the new feature Runtime Context. It provides a structured way for tools such as Cursor and GitHub Copilot to inspect how code behaves after deployment. It targets a persistent weakness in AI-assisted development, where generated code often passes tests in the Integrated Development Environment but fails once exposed to real traffic and dependencies.
Research from Stanford and Google has highlighted high failure rates for AI-generated code under real-world conditions. Engineering teams report that they spend many hours each week debugging and refactoring such output.
Lightrun positions Runtime Context as a link between the IDE, the AI assistant, and running applications. The system gives AI agents and developers access to data from staging, pre-production, and production environments in a controlled way.
Developers can ask their AI assistant to review staging traffic before writing a new module. They can request an investigation of a production incident or ask for additional instrumentation that validates application behaviour. The AI assistant then acts against live telemetry rather than relying only on static code analysis or unit tests.
The Model Context Protocol implementation acts as a secure bridge between AI tools and running services. Lightrun's software allows AI agents to insert logs, add tracing, and capture snapshots in real time. It also supports safe inspection of issues and automatic proposal of fixes without requiring engineers to recreate errors in a lab environment.
Teams can apply suggested changes, perform a single rebuild, and re-check behaviour quickly in the target environment. Lightrun said this workflow can compress issue resolution windows from several days to minutes in some cases.
The company said AI tools using the Runtime Context model can now trigger remote debugging sessions in staging, pre-production, or production. They can access telemetry that reflects live workload patterns. They can also propose fixes that reflect actual runtime behaviour rather than inferred assumptions.
Lightrun said this approach supports delivery of code that is more stable and closer to deployment-ready state. The company expects customers will see faster debugging cycles and higher reliability at release time.
The launch comes as software teams step up use of AI coding tools in day-to-day development. Many organisations now treat systems such as GitHub Copilot as standard components in their toolchains. At the same time, concerns about the reliability and maintainability of AI-written code have grown.
Lightrun focuses on what happens after code leaves the IDE. Its platform connects production and pre-production environments with development workflows. The Runtime Context feature extends this link to AI agents as well as to human engineers.
"AI has taken over much of the creative part of coding," said Ilan Peleg, CEO and co-founder, 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 company said its customers use the underlying platform to detect issues earlier and to validate fixes in live environments without redeploying from scratch. It said this helps remove verification bottlenecks that slow software teams and create queues of unresolved incidents.
Lightrun describes itself as a continuous reliability provider that gives developers and AI agents real runtime context at code level. Its platform integrates debugging tools, runtime insights, and AI-assisted root cause analysis into day-to-day engineering processes.
The business was founded in 2019 and has raised about USD $110 million from investors. It works with large enterprises including Salesforce, Microsoft, Citi, SAP, and AT&T.
The company has set out a long-term vision in which software issues resolve in a largely autonomous way, without user disruption. It plans to extend its Runtime Context model as AI-assisted development becomes more common in production systems.