Validate New Features
with Live Runtime Behavior
Confirm how new code executes across services, data, and dependencies before full rollout.
Launch features proven in running environments
New code interacts with real data, real traffic, and real dependencies. Validation must happen in the environment where the code runs.
Trace new execution paths
See how new branches, flags, and inputs execute under live execution conditions.
Measure downstream impact
Observe how changes affect APIs, databases, and third-party services.
Confirm system stability
before exposure expands
Validate latency, error rates, and side effects before scaling rollout.
How Lightrun verifies features safely
Use dynamic instrumentation and a read-only sandbox to test new code safely across production environments.
Instrument code
at the point of change
Add dynamic logs, traces, metrics, or snapshots exactly where new code runs. No rebuilds. No redeploys
Observe execution
under real traffic
See how new code paths, feature flags, and downstream calls behave under real production load.
Validate code in an
isolated sandbox
All testing runs inside a permission-controlled, read-only remote environment that prevents code mutation.
Why AI-native reliability requires runtime context
AI-native engineering is only as reliable as the runtime truth it consumes.
Bridge the verification bottleneck
Prove that code behaves correctly under real traffic conditions with runtime context
Validate AI-generated code safely
Ensure AI-written logic executes correctly by observing live branch decisions and variables without triggering a rebuild cycle
Release changes with confidence
Identify hidden regressions and validate remediation safely in a read-only sandbox before expanding feature exposure