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Entering the AI software factory era
What automating the SDLC at LaunchDarkly taught me about speed, control, and the job of an engineer.
Cameron Etezadi
Stories from the Factory Floor: Building a software factory on our scariest code
We pointed coding agents at our oldest, most business-critical frontend. Here’s what it taught me about what a healthy AI software factory actually looks like.
Agent Optimization: Define what better means, and let AgentControl find it
Agent Optimization, now in beta in AgentControl, automatically searches for a better agent configuration against criteria you define.
Product Updates
See what's launched lately.
Stories from the Factory Floor: Our AI software factory saved me from an incident and I lived to tell the tale
Last summer, I shipped what I thought was a routine cleanup to production. It turned out to be a bug. But before the vast majority of users ever saw it, our AI software factory caught it and rolled back my change automatically.
Alex Engelberg
Podcast recap: Observability won’t save your agents
On a recent episode of the MonkCast, Marek Poliks spoke with James Governor about why governing agents from the outside leaves teams perpetually one step behind.
LaunchDarkly
Agent Optimization: Define what better means, and let AgentControl find it
Agent Optimization, now in beta in AgentControl, automatically searches for a better agent configuration against criteria you define.
Kelvin Yap
Stories from the Factory Floor: Building a software factory on our scariest code
We pointed coding agents at our oldest, most business-critical frontend. Here’s what it taught me about what a healthy AI software factory actually looks like.
Alexis Georges
Stories from the Factory Floor: Empowering agents with LaunchDarkly MCP tools
A new capability on the LaunchDarkly MCP server offers a practical look at what an automated software factory could look like in practice.
Ramon Niebla
Why AI deployment breaks standard CI/CD
Learn why AI deployment can break standard CI/CD and how runtime controls, shadow testing, rollouts, and rollback reduce risk.
Scarlett Attensil
Entering the AI software factory era
What automating the SDLC at LaunchDarkly taught me about speed, control, and the job of an engineer.
Cameron Etezadi
Observability is not enough
With runtime control, teams can extend observability by moving beyond reactive monitoring and toward proactive remediation.
Betsy Sallee
Warehouse-native experimentation comes to BigQuery, Databricks, and Redshift
Analyze your experiments on the same trusted data your business already runs on, so results never come with an asterisk.
Lavanya Sureka
Feature flags were always important. SRE agents make them essential.
AI-powered SRE agents are getting very good at identifying when something is wrong in production. What they haven't solved, however, and what most teams have dramatically underinvested in, is what happens after the agent knows.
Cameron Etezadi
Why LaunchDarkly is standardizing on New Relic
Today, we are announcing that LaunchDarkly is officially moving its primary observability and telemetry workloads to New Relic.
Cameron Etezadi
Speed isn't the risk. Lack of control is.
Why controlling code and agents in the AI era matters—and why we built AgentControl.
Kellye King
The Complete AI Experimentation Guide: Test, compare, validate, and ship safely
Artificial intelligence tools aren’t like traditional software.
Scarlett Attensil
Release management tools: What they are and how they work
Understanding the control layer between your CI/CD pipeline and your users.
Jesse Sumrak
Feature flags vs. feature branching: Why you need both for faster, safer releases
Learn where each fits into your delivery workflow.
Jesse Sumrak
MLOps lifecycle: Stages, workflow, and best practices
Understand the MLOps lifecycle from data preparation to monitoring.
Scarlett Attensil
AI pipeline: Preventing drift in production systems
Learn why uncontrolled AI pipeline changes can cause failures in prod.
Scarlett Attensil
Adaptive Triggers: AI that corrects itself in production
Adaptive Triggers is now available in closed beta.
Kelvin Yap
The next era of software needs runtime control
Edith Harbaugh
Introducing AgentControl
AgentControl is the operational layer for managing agents in production.
Kelvin Yap
LLM observability: Tutorial and best practices
LLM observability analyzes how models behave across development, testing, and production.
Scarlett Attensil
LLM pricing comparison: Tutorial and best practices
Large language models (LLMs) power a wide range of AI applications today.
Scarlett Attensil
Agent graphs bring control and visibility to multi-agent AI workflows
Agent graphs bring real-time control to multi-agent AI workflows.
Kelvin Yap
How to automate runtime control with kill switches, progressive rollouts, and user targeting
These strategies can help you design for control in production.
Megan Moore
