You can't control what you can't see
Blog post from LaunchDarkly
LaunchDarkly’s latest updates focus on bringing monitoring, remediation, AI-assisted maintenance, and experimentation closer to the point of software release. Adaptive triggers can automatically change a feature flag when observability signals such as error rates cross configured thresholds, while Session Replay connects flagged audiences with recordings, logs, traces, and flag evaluations to aid investigation. Vega Flag Cleanup uses an AI agent to identify stale flags, make code changes, and open reviewable pull requests, with scheduling and safeguards for critical environments; MCP integration also allows agents such as Claude, Cursor, and Codex to access experimentation and observability data and initiate triage from tools like Slack or PagerDuty. Warehouse-native Experimentation keeps analytics and experiment metrics in a company’s existing data warehouse, supporting Snowflake, BigQuery, Databricks, and Redshift, while allowing teams to add metrics or segmentation attributes during active experiments without restarting them.
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