Information Density: LaunchDarkly – Signal Evidence & AI Readability

[H1] Move at AI speed.Stay in control.
The runtime control layer for AI development, de-risking releases and enabling systems that heal and optimize themselves.By supplying my contact information, I authorize LaunchDarkly to contact me with personalized marketing communications about our products and services. See our Privacy Policy for more details, or Opt-Out at any time.Group ByChat CompletionCode AssistantSummarizationEmbeddingChat CompletionCode AssistantSummarizationEmbeddingCost$3,319-13.7%3000Apr 12Apr 14Apr 16CodeControlToxicity0.1-6.0%3000Apr 12Apr 14Apr 16Apr 18Apr 20Apr 22Apr 24Apr 26Apr 28Apr 30Apr 12Apr 14Apr 16Apr 18Apr 20Apr 22Apr 24Apr 26Apr 28Apr 30Group ByConfigTrendsModel Distributionlast 7dGPT-5.550%Claude Sonnet 4.635%Claude Opus 4.710%Text Embedding 35%AgentControlFinancial Analyst AgentAll variations ⌄1 ToolAvg. Satisfaction91xlast 7d
[IMG: Veeam]
[IMG: epilot GmbH]
[IMG: Hireology]
[IMG: Relay Network]
[IMG: Poka]
[H3] Ship AI-built code with confidence.
Progressively release changes and roll back instantly based on real-time impact.Learn more
[H3] Control and govern AI agents in production.
Automatically keep agents on track, mitigating bad behavior and steering responses in real time.Learn more
[H3] Optimize AI performance and cost.
Test prompts and models in production and dynamically route traffic to the best option.Learn more
[H3] Enable self-healing systems.
Instantly remediate failing code and misbehaving agents (without human intervention).Learn more
[H3] Experiment continuously.
Experiment with code and agents in production and optimize based on real-world results.Learn moreCode + Agents
[H2] Ship with AI that does what you expect—however you build.
[H2] Control your code in production.
Ship confidently with automatic recovery and continuous improvement built in.Learn about CodeControlFeature FlagsTargeting & Segments Progressive rolloutsAutomated rollbacksFeature monitoringObservabilityError monitoringLogs & tracesSession replayAuto-remediationExperimentationA/B/n testingMulti-armed bandits (MAB)HoldoutsWarehouse nativeSelf-healing
[H2] Control your agents in production.
One place to keep your agent behavior under control.Learn about AgentControlOnline evalsOffline evalsAdaptive triggersProgressive rolloutsAgent observabilityAI InsightsCustom judgesLLM PlaygroundLLM tracesA/B/n testingAgent graphsApprovalsAgent definitionsRBACPrompt snippetsExperimentationSee for yourself
[H2] Platform Overview Demo
[H2] Built for developers
[H2] and their agents.
[H3] Dive into best practices.
See end-to-end examples of building on the LaunchDarkly platform.Browse tutorials
[H3] Go agent-native.
Learn more about working with LaunchDarkly using your agents.View agent integrations
[H3] Copy, paste, go.
Drop this prompt into your favorite coding assistant and get up and running with AI in seconds.Onboard me to LaunchDarkly. Start by installing the onboarding skill: `npx skills add launchdarkly/agent-skills --skill onboarding -y`. source-launchdarklyCopy to clipboard
[H2] Savage X Fenty keeps shoppers engaged with rapid, reliable experiments.
[IMG: Savage X Fenty case study]
Read story
[IMG: Savage X Fenty]
LaunchDarkly gave the business teams the confidence that experiments could be run reliably and the data could be trusted.Alan ChangProduct Management Director, Savage X FentyImprovement in site performance15%
[IMG: Savage X Fenty case study]
Read storyPoka goes “flag-first” to transform its release processes and AI innovation.
[IMG: Poka case study]
Read story
[IMG: Poka]
If prompts were only on the backend, only the backend people could modify them. But since they're a flag in LaunchDarkly, the product managers, frontend developers, or even the designers might have access to modifying them if they want to test something out.Edmund LamStaff Software Developer, Poka
[IMG: Poka case study]
Read storyDior shortens time to market from 15 minutes to instant updates.
[IMG: Dior case study]
Read story
[IMG: Dior]
LaunchDarkly allowed us to progressively deliver features with confidence, creating a safety net for developers.Fabien GasserRetail Lead System Architect, DiorMinutes to release< Zero
[IMG: Dior case study]
Read storyParamount improves developer productivity
[IMG: Paramount case study]
Read story
[IMG: Paramount]
It’s one of the 'three legs of the stool,' as I like to say. One leg is CI/CD, another is automated testing, and the third is LaunchDarkly.Dan SkaggsTechnical Director, Content Engineering, ParamountDeployments per day6-7
[IMG: Paramount case study]
Read storyHireology builds safe, scalable AI features.
[IMG: Hireology case study]
Case study
[IMG: Hireology]
In less than 13 seconds, I can test 3 verticals, 10 tests each with LaunchDarkly. In the time it takes to generate one job description, I’ve tested all iterations programmatically.Sam ElliottStaff Quality Assurance Engineer, HireologyChange failure rate8%
[IMG: Hireology case study]
Case study
[H3] Check out the blog.
BlogRead about LaunchDarkly news, product updates, and more.
[H3] Explore our docs.
DocsLearn best practices for getting started with LaunchDarkly.
[H3] Watch on demand.
VideosCheck out demos and tutorials to see LaunchDarkly in action.
[H3] Connect at events.
EventsExplore ways to connect with us in person and virtually.

[H1] Get started with a demo of LaunchDarkly.
[H5] We'll tailor it to your workflows and show how you can move faster and reduce risk in production.
[IMG: Ship AI-built code with confidence.]
Ship AI-built code with confidence.
[IMG: Control and govern AI agents in production.]
Control and govern AI agents in production.
[IMG: Optimize AI performance and cost.]
Optimize AI performance and cost.
[IMG: Enable self-healing systems.]
Enable self-healing systems.
[IMG: Experiment continuously.]
Experiment continuously.
[H2] Get started with a demo of LaunchDarkly.
Must be valid email. (example@yourdomain.com)Please enter a work email for a demo.This field is requiredThis field is requiredThis field is requiredThis field is requiredMust be a phone number. eg: 503-555-1212Book a demoBy supplying my contact information, I authorize LaunchDarkly to contact me with personalized marketing communications about our products and services. See our Privacy Policy for more details, or Opt-Out at any time.©2026 Catamorphic Co.

[H1] Control your code in production.
Ship AI-generated code without worrying about a 2 a.m. fire drill with CodeControl.Book a demoTry it free
[IMG: Veeam]
[IMG: epilot GmbH]
[IMG: Hireology]
[IMG: Relay Network]
[IMG: Poka]
[H2] Ship confidently, with automatic recovery and continuous improvement built in.
01Control→
[H4] Own what ships—down to the last detail.
Configure flags and rules before anything reaches users. Control exposure with targeting, segmentation, and progressive rollouts, so every change is intentional, precise, and adjustable in real time.02Protect→
[H4] Go beyond flags with resilience built in.
Guard every release automatically. Set performance thresholds and monitor release health in real time with guarded releases. Trigger kill switches or automated rollbacks the moment something goes wrong—limiting blast radius and keeping systems stable.03Understand→
[H4] Know exactly what changed—and why it matters.
Track errors, metrics, and user impact tied directly to releases. Correlate logs, traces, and session replay to understand behavior in real time so you’re never guessing what caused an issue.04Remediate→
[H4] Fix problems faster than you thought possible.
Resolve issues at the point of release with Vega, the LaunchDarkly observability agent that explains what broke, why, and how to fix it. Use AI-powered investigation and guided remediation to identify root causes and resolve issues without delay.05Adapt→
[H4] Turn every change into measurable improvement.
Run experiments, measure impact with real data, connect to your Warehouse, and optimize outcomes over time. Use statistical models, guardrails, and automated optimization to roll out winners and improve performance continuously.
[H3] Own what ships—down to the last detail.
Configure flags and rules before anything reaches users. Control exposure with targeting, segmentation, and progressive rollouts, so every change is intentional, precise, and adjustable in real time.
[H2] Control your deployment.
Roll out features confidently with targeting, progressive delivery, and real-time monitoring.
[H3] Progressively roll out changes and target users precisely.
Use attributes like location, plan type, or OS with real-time dynamic adjustments—without redeploying.
[H3] Define performance guardrails.
Set thresholds (like errors and latency) with out-of-the-box templates for flexible monitoring windows from days to minutes.
[H3] Ensure critical metrics are monitored.
Easily configure metrics to events via LaunchDarkly APIs, SDKs, Sentry, or OpenTelemetry, and automatically track rollout health for safer, more successful completions.
[H2] Rest easy, we’re on guard.
[H3] Track release progress.
Automated Rollout and Monitoring help ensure error-free, more successful completions.
[H3] Monitor performance in real time.
Regression Detection alerts (configurable PagerDuty and Slack notifications) help enable prompt remediation.
[H3] Respond to errors in milliseconds.
Automated Rollbacks help revert to the last good state when application performance thresholds are breached.
[H2] See what broke—and why.
[H4] Surface frontend issues in real time.
Use Error Monitoring tied to feature flags, complete with stack traces, breadcrumbs, and alerts.
[H4] Reproduce bugs. Understand user behavior.
Get session replays, console logs, and network activity.
[H4] Accelerate triage with a single view.
Connect errors, sessions, and feature rollouts so your team can help prevent (and fix) issues without the guesswork.
[H4] Surface frontend issues in real time.
Use Error Monitoring tied to feature flags, complete with stack traces, breadcrumbs, and alerts.
[H4] Reproduce bugs. Understand user behavior.
Get session replays, console logs, and network activity.
[H4] Accelerate triage with a single view.
Connect errors, sessions, and feature rollouts so your team can help prevent (and fix) issues without the guesswork.
[H4] Surface frontend issues in real time.
Use Error Monitoring tied to feature flags, complete with stack traces, breadcrumbs, and alerts.
[H4] Reproduce bugs. Understand user behavior.
Get session replays, console logs, and network activity.
[H4] Accelerate triage with a single view.
Connect errors, sessions, and feature rollouts so your team can help prevent (and fix) issues without the guesswork.
[H3] Before fully adopting LaunchDarkly, our engineers were spending a lot more time babysitting releases, making manual changes, and watching metrics. Now, they can focus on building the product rather than constantly monitoring it.
Adam KadzbanPrincipal EngineerAutomated risk controls to deliver safer software releases
[IMG: Relativity automates risk controls to deliver safer software releases.]
[IMG: Relativity automates risk controls to deliver safer software releases.]
Read story

[H1] Control your agents in production.
Agent behavior shifts in production without warning. AgentControl helps keep agents on track, blocking bad behavior and steering responses in real time.Book a demoTry it freeGroup ByChat CompletionCode AssistantSummarizationEmbeddingChat CompletionCode AssistantSummarizationEmbeddingAvg. Satisfaction91xlast 7dCost$3,319-13.7%3000Apr 12Apr 14Apr 16Model Distributionlast 7dGPT-5.550%Claude Sonnet 4.635%Claude Opus 4.710%Text Embedding 35%Financial Analyst AgentAll variations ⌄1 Tool
[IMG: epilot GmbH]
[IMG: Hireology]
[IMG: Poka]
[IMG: Relay Network]
[IMG: Veeam]
[H2] One place to control agent behavior.
01Agents that self-heal.→
[H4] Set your thresholds with Adaptive Triggers. Your agents handle the rest.
Other tools tell you when an agent fails. AgentControl fixes it automatically. When a response drops below your quality threshold, it escalates to a more capable config—within the same conversation turn, before the customer sees anything.02Benchmark changes with offline evals.→
[H4] Use offline evals to stop guessing which version performs better.
Run offline evals of prompt and model variants against your test datasets before anything ships. LLM judges score each candidate across quality, cost, and whatever business metrics your use case demands. Ship the winner with confidence and know exactly why it won.03Iterate in milliseconds, not sprints.→
[H4] Iterate without the deploy cycle.
Every prompt change used to mean a redeploy. Now it propagates in under 200ms—and the deployment pipeline never gets involved.04Monitor quality with online evals.→
[H4] Online evals score every response. AI Insights connects the dots.
Track the metrics that matter to your system (cost, quality, latency, error rate, and anything your judges are measuring) across your agents. When the numbers move, AgentControl shows you which configuration change caused it, so you know what to fix, not just what broke.05Visualize your entire agent system.→
[H4] Map your entire agent system with agent graphs.
Map your entire multi-agent system (nodes, connections, and performance metrics) from one place. Configure each agent independently, trace behavior through the full graph, and see where things are breaking down.
[H3] Set your thresholds with Adaptive Triggers. Your agents handle the rest.
Other tools tell you when an agent fails. AgentControl fixes it automatically. When a response drops below your quality threshold, it escalates to a more capable config—within the same conversation turn, before the customer sees anything.
[H2] Stop hardcoding.Start iterating faster.
Replace hardcoded config with a single SDK call wherever you initialize an agent. Manage prompts, models, and tools in AgentControl, instead of scattered across every service in your stack.BEFOREtriage_agent.pytriageAgent.ts# config buried in code — any change = redeploy
# repeat this for every agent — every team, every update cycle
MODEL = "gpt-4o"
TEMPERATURE = 0.3
MAX_TOKENS = 512
SYSTEM_PROMPT = """You are a triage agent for a medical
insurance company. Classify the query and route to:
provider_agent, policy_agent, or billing_agent."""
def run_triage(query: str) -> str:
return openai_client.chat.completions.create(
model=MODEL,
temperature=TEMPERATURE,
max_tokens=MAX_TOKENS,
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": query},
],
).choices[0].message.contentAGENTCONTROL SDKPythonTypeScript# one-time SDK setup
ctx = Context.builder("user-123").kind("user").build()
def handle_model_call(config, tracker):
response = tracker.track_openai_metrics(
lambda: openai_client.chat.completions.create(
model=config.model.name,
messages=[m.to_dict() for m in config.messages] + [
{"role": "user", "content": query}
],
)
)
return response.choices[0].message.content
# each agent is two lines — config lives in AgentControl
config, tracker = aiclient.completion_config(
"triage-agent", ctx, fallback_config
)
handle_model_call(config, tracker)Works with leading providersand frameworks.
[H2] Built for every stage of agent development.
Configure, benchmark, release, observe, and iterate—everything you need to build and run agents in production, without stitching together a different tool for each.01Configure
[H3] Define agent behavior from one place.
Model settings, prompts, tool configs—all in a central store, separate from the code that deploys them. Shared prompt components propagate across every config automatically. Every change is versioned, auditable, and access-controlled.02Benchmark
[H3] Nothing ships without clearing your quality bar.
Run offline evals of prompt and model variants against your golden datasets before anything ships. LLM judges score each candidate against your defined thresholds—and only what clears the bar gets to production.03Release
[H3] Guarded rollouts with automatic rollback.
Roll out a prompt or model change progressively to users—no deployment required. Traffic splits and user targeting let you expand at your own pace. Quality metrics watch every stage: Drift triggers automatic rollback before it reaches more users, and critical failures halt the rollout immediately.04Observe
[H3] Understand what every agent is doing and why.
Full traces across every agent invocation: What was called, in what order, and how long each step took. Online evals run continuously against production traffic, scoring for quality, cost, and any custom metrics you define. When metrics shift, you'll know which config change caused it.05Iterate
[H3] Run experiments on live traffic and ship what wins.
A/B and multi-armed bandit experiments on live traffic, scored by LLM judges and business metrics. When a winner emerges, it ships automatically. Every experiment leaves you with better data for the next.
[H2] Enterprise-ready from Day 1.
AgentControl is built on the same infrastructure LaunchDarkly uses to serve 50 trillion flag evaluations a day across some of the largest engineering teams in the world. That means reliability, security, and compliance are solved problems before a single agent goes live.50T+Flag evaluations per day< 200msConfig propagation, globally99.99%Enterprise uptime SLASOC 2 Type IICertifiedISO 27001CertifiedISO 27701CertifiedFedRAMPModerate ATO
[H2] Hireology builds safe, scalable AI features.
[IMG: Hireology case study]
Case study
[IMG: Hireology]
In less than 13 seconds, I can test 3 verticals, 10 tests each with LaunchDarkly. In the time it takes to generate one job description, I’ve tested all iterations programmatically.Sam ElliottStaff Quality Assurance Engineer, HireologyChange failure rate8%
[IMG: Hireology case study]
Case studyRelay Network ships secure GenAI capabilities for regulated clients with AgentControl.
[IMG: Relay Network case study]
Read story
[IMG: Relay Network]
We launched our generative AI feature without turning it into a massive engineering project. Product teams can experiment directly, so we're not spending hours deploying updates just to test prompt changes.Brendan PutekDirector of DevOps, Relay Network
[IMG: Relay Network case study]
Read storyPoka goes “flag-first” to transform its release processes and AI innovation.
[IMG: Poka case study]
Read story
[IMG: Poka]
If prompts were only on the backend, only the backend people could modify them. But since they're a flag in LaunchDarkly, the product managers, frontend developers, or even the designers might have access to modifying them if they want to test something out.Edmund LamStaff Software Developer, Poka
[IMG: Poka case study]
Read story