Real-time debugging in live production systems
Power your AI agents to diagnose issues and validate fixes against real execution paths without rebuild cycles or local reproductions.
Debug production systems without reproductions
Stop recreating incidents locally or waiting for logs to catch recurring issues.
See the exact
failing path
Trace real execution under live traffic instead of reconstructing behavior from partial logs.
Inspect state at
the point of error
View variables, call stacks, inputs, and downstream responses in context.
Eliminate
redeploy loops
Investigate and validate hypotheses directly in live environments without rebuild cycles or restarts.
How Lightrun resolves live production failures
Lightrun combines natural language investigation with dynamic runtime instrumentation
to diagnose and validate fixes directly in live environments.
Describe your issue
in natural language
Explain the problem, or paste a ticket into your AI agent’s chat and it uses Lightrun’s live debugging skill to query, hypothesize, and investigate using runtime context.
Add targeted
instrumentation
The agent places dynamic logs and metrics at precise execution points, based on likely root causes, to confirm or rule out each hypothesis.
Capture live
execution evidence
Capture variables, branch decisions, call stacks, and downstream responses at the exact moment the issue manifests under real traffic to understand full impact and blast radius.
Get a structured diagnosis
not just data
The investigation ends with a confirmed diagnosis, confidence level, evidence summary, and a concrete fix proposal, ready to act on, without a single redeployment.
Why live debugging improves engineering performance
Shorten time from alert to resolution
Ground remediation decisions in live execution evidence.
Eliminate redeploy loops during incidents
Investigate and validate fixes without rebuilding code.
Ground AI agents in live runtime context
Ensure every hypothesis is validated against actual, not expected behavior.
Frequently asked questions about live runtime debugging
Real-time debugging in production systems is the practice of investigating live application behavior, variables, call stacks, branch decisions, and execution counts, without stopping the application, redeploying code, or reproducing the issue locally. Unlike traditional debuggers, it works directly against running traffic in staging or production environments.
You can debug production code without redeploying by using dynamic instrumentation, adding logs, snapshots, and metrics directly to a running application through a runtime agent. Lightrun’s agent attaches to live services in a read-only sandboxed environment and inserts instrumentation at specific execution points on demand, with automatic cleanup after the investigation. No code changes or restarts are required.
Yes, when instrumentation is sandboxed and governed. Lightrun runs all instrumentation in an isolated sandbox outside the main execution path, so logs, metrics, and snapshots never pause threads or alter runtime state. A central Management Server brokers every request, enforces access policies, and automatically redacts sensitive data before it reaches the client.
Lightrun’s Live Runtime Debugging Skill guides an AI agent through a structured investigation of live runtime issues using Lightrun MCP. It moves from a problem statement to a diagnosis by forming hypotheses, running a preflight check to discover available runtime targets, placing targeted instrumentation to collect evidence, and closing with a confirmed diagnosis, confidence level, and fix proposal, without redeploying the application.
The Live Runtime Debugging Skill works with any MCP-compatible AI agent, including Claude Code, Cursor, Codex, Gemini, Kiro and many others. It uses the Lightrun MCP server to connect AI assistants to live runtime context. Setup requires installing Lightrun MCP and authenticating it within your AI client.
When given a problem statement, the Live Runtime Debugging Skill guides the AI agent to list at least two plausible hypotheses before touching any runtime tool. It then runs a preflight check to identify available targets, places instrumentation only where needed to confirm or rule out each hypothesis, and closes with a structured handoff: diagnosis, confidence level, evidence summary, and a concrete fix proposal.