LaunchDarkly unveiled key new AI capabilities and integrations that let developers ship software at even higher velocity while keeping risks at bay.
These updates further the company’s vision for Self-Healing Software that allows engineers to focus on delivering amazing user experiences instead of anxiously awaiting late-night calls to fix bugs and outages.
The new observability updates provide closed-loop automation for de-bugging and quality control that transform how companies approach software quality and reliability.
The updates include:
- Live view of feature performance: Adding session replay, error monitoring and APM data directly into the rollout surface gives developers a live view into how changes are performing without needing to wait on legacy alerts or hunt across APM dashboards. This closes the loop between code change and customer impact, dramatically reducing mean time to repair and building confidence to ship more often.
- Regression attribution to metrics: Guarded Releases connect the dots between what changed (your feature flag) and what broke (your metric). No more guesswork, blame-pong, or digging through dashboards at 2 a.m. When a metric regresses, LaunchDarkly shows you the exact flag change that caused it and lets you roll it back with one click.
- Session replay: provides full context into how a release impacts real users right down to clicks, rage-scrolls, and form abandons. You can finally pair telemetry data with human-readable truth. It's the fastest way to diagnose what actually happens when a release behaves badly, especially when the bug doesn’t trigger an alert.
- Upcoming AI-Powered Diagnostics: The upcoming Vega AI agent will help eliminate the traditional "needle in a haystack" debugging process. Vega analyzes logs, traces, metrics, and session replays to identify root causes, generate timelines of what broke and why, and surface recommended code changes—turning noisy production data into actionable insights.
“While iterating quickly on products is paramount, organizations are becoming terrified of their own speed — they can find themselves flying blind on how their software is performing as they ship,” said Jay Khatri, Head of Observability at LaunchDarkly. “One bad release during peak season can cost millions in revenue and customer trust, which is why we’re so focused on moving from reactive damage control to proactive confidence.”
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