Kiro Can Now Use Lightrun via MCP
Feb 19, 2026
AI code assistants transformed how software is written. They did not transform how it fails.
Today, we’re announcing a new MCP integration between Lightrun and Kiro.
Kiro now gains live runtime visibility through the Lightrun MCP, grounding 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 helps teams move from specification to production faster by turning intent into working code. But until now, like every AI coding assistant, Kiro had a critical blind spot. It could reason about code, but it could not see how that code behaves once it is deployed.
That visibility gap is where many reliability issues begin.
Close the runtime visibility gap with Lightrun MCP
By authorizing the Lightrun MCP in Kiro, you can close the gap. The Lightrun MCP server supplies Kiro with live runtime context, and allows it to reason over real execution data rather than inferences from static information. This context comes directly from running systems and is delivered on demand, without redeploying code or impacting users.
Kiro remains the AI accelerator.
Runtime context is now its source of truth.
Design better code using runtime context
Specifications are fixed. Production is not.
A timeout may look safe and a retry strategy reasonable until real traffic proves otherwise. As AI-assisted development accelerates, teams are often forced to assume generated code will behave correctly under live conditions.
Runtime context removes the need to assume.
With Lightrun MCP, Kiro can inspect live execution paths, observe how data actually flows through services, and see which conditions occur in real environments. As new code is designed, Kiro can reasons over real system behavior and architecture, basing its decisions in runtime evidence rather than theoretical models.
This shifts reliability into the design phase, helping teams build code that reflects how their systems actually behave.
Investigating issues using runtime evidence
Until now, AI coding assistants have not been able to see what happens once code leaves the IDE.
When systems fail or behave unusually, engineers are forced into manual investigations, often of unfamiliar code. The scan logs, switch between tools, and add instrumentation just to confirm a hypothesis.
This is often where teams lose much of the time that AI assistance initially saved.
With Lightrun MCP, investigation no longer starts with guesswork. Kiro can independently reason over live runtime context to observe variable values at failure points, confirm which execution paths were taken, and verify hypotheses about root causes against real system behavior.
Engineering teams stop guessing and start seeing what is actually happening. Feedback loops shorten without adding operational overhead or requiring redeploys.
Validate changes before they impact users
The riskiest moment in development is merging a change that behaves differently under live conditions and impacts users.
With runtime context available, Kiro helps validate changes against live, sandboxed execution behavior before users are affected. Fixes suggested by the assistant or the engineer can be evaluated based on whether they stabilize execution paths and perform as expected under real traffic.
Validation becomes evidence-backed rather than assumptive, reducing the number of surprise regressions, allowing teams to trust their AI-assisted code running in production.
How to set up Lightrun MCP in Kiro?
To give an AI coding assistant like Kiro access to runtime context, three components are required.
- A running application
Any service where Lightrun is already attached or can be attached, in staging or production-like environments. - The Lightrun MCP server enabled
This acts as the bridge between your running code and the AI assistant, exposing live runtime context in a safe, controlled way. - An MCP-enabled AI client
Once connected, your AI assistant can query runtime context such as variable values, execution paths, and call stacks, without redeploying or changing code.
Once these are in place, Kiro can access live runtime context without altering the code or requiring redeployments.
Get started today
The Lightrun MCP integration with Kiro is available now.
For AI to let your teams move fast, it needs evidence.
- Learn more about how to set up the Lightrun MCP server.
- Quickstart with setting up the MCP.
Frequently asked questions about Runtime Context
Runtime Context is the live, execution-level state of a running application (variables, call stacks, metrics) available to an AI during its reasoning loop to verify code functionality.
It provides ground truth allowing AI to verify environmental conditions like database latency and data shapes rather than inferring them from static documentation.
Lightrun Runtime Context MCP allows AI assistants to securely interrogate live services and validate running code’s behavior in staging, QA, pre-production, and production environments without a redeploy.
