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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
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
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
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
Agent Optimization: Discover better agent configurations automatically
Agent Optimization is now available in private beta for eligible customers.
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
Orchestrate and safeguard AI agents with AI Configs
LaunchDarkly AI Configs helps you control AI agents at runtime.
LaunchDarkly
Online evals in AI Configs is now GA
Online evals in AI Configs help you define and monitor quality in production.
Kelvin Yap
AI-generated code ships fast, but runtime control hasn’t kept up
AI is speeding up code generation, but control in production is lagging behind.
Introducing LLM Playground for AI Configs
Test, compare, and trace LLM prompt and model variations before they reach production.
Kelvin Yap
LLM Evaluation: Tutorial & Best Practices
Learn how to properly evaluate large language models in various applications and contexts.
LaunchDarkly
Creating better runtime control with LaunchDarkly and AWS
Ship bold AI changes without the guesswork.
Neha Julka
Online evals: LLM-as-a-Judge
Online evals in AI Configs give teams quality signals to successfully ship AI changes.
Kelvin Yap
