Feature Management & Experimentation

Release & experiment in production at the speed of AI

Q: What is Feature Management & Experimentation?

Feature Management & Experimentation extends traditional feature flag capabilities to cover the full lifecycle of AI agents and application behavior. It lets teams control feature releases and AI agent rollouts using the same progressive delivery primitives: percentage-based targeting, kill switches, real-time config updates, and metric-driven decisions.

Q: What are use cases for feature flags beyond simple on/off toggles?

High-value use cases include AI configs and configs, and safely executing infrastructure migrations by validating new systems through dual reads, verifying they match legacy behavior, gradually ramping write traffic, and maintaining rollback capability throughout.

Q: How does feature management enable safe Testing in Production?

Feature flags separate the deployment of code from its release to users. They allow new features to be deployed but hidden, enabling controlled exposure via gradual rollouts or canary releases. If an issue is detected, the flag can be instantly turned off without requiring a full application redeployment.

Q: How can we proactively manage and eliminate feature flag technical debt?

Harness Worker Agents like FME's Feature Flag Cleanup Agent integrates directly into your pipelines to automatically detect stale flags based on rollout status, targeting rules, and inactivity metrics. Teams can run scheduled scan-only audits to build trust, then trigger cleanup workflows that generate individual draft pull requests for safe human review.

Q: What is Harness's approach to feature flagging?

Harness Feature Flags lets you control features without deploying new code by decoupling deploy from release. SDKs integrate into your application code, allowing you to wrap features in flags that can be toggled on or off at runtime. Key differentiators include CI/CD pipeline integration, built-in governance with approvals and audit trails.

The runtime management platform. Feature flags, configs, and AI configs, all governed, all targeted, all measured, all delivered at scale.

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<200msconfiguration propagationwithout redeploying

50×faster releaseswith feature flags

95%reduction in incidentsattributed to code changes

Why teams switch

Your flag tool isn't a configuration platform.

Most teams outgrow their feature flag tool before they realize it. Runtime configuration, AI behavior, and experimentation need a foundation, not a bolt-on.

Runtime Control

Flags only. Configs and AI parameters remain fragmented, manual, and unmanaged.

Manage flags, configs, and AI configs without redeploys, under one governance model.

Governance

Flags, configs and AI changes lack controls, audits, and approvals.

Govern every runtime change with RBAC, policies, approvals, and audit trails.

Experimentation

Runtime configuration changes ship without measurement or attribution.

Experiment across flags, configs, and AI configs with one statistical engine.

Agent Development LifecycleNEW

Update AI behavior at runtime with built-in experimentation

Iterate, measure, and control AI agents at runtime: swap models and prompts with AI Configs, then validate every change with AI Experiments so you can promote with confidence.

Read about AI Config Management Learn more about the Agent DLC

AI Configs.Use structured templates, import a prompt directly from AI Evals with environment variables and one-click promotion.

AI Experiments.Validate business impact across variants, track cost and latency side by side without waiting for a redeploy.

Enterprise Governance.Every change is orchestrated through the same pipelines, policies, and RBAC that govern your deployment and releases.

Instant Changes and Rollbacks.Revert to a previous prompt or model in one click leveraging Harness high-availability feature flag infrastructure.

AI DLCNEW

Extends AI to the SDLC for configs, flags, and experiments.

Feature flags, configs, experimentation, RBAC, policies, and a global delivery network already govern how your code ships safely. Harness infused AI into the same lifecycle you already run, so every config and feature release is governed exactly like every test and deployment.

Learn more about the AI DLC

Feature Flags.Progressive delivery for AI driven feature releases: ship dark, release when you're ready, and roll back in seconds.

Configs.Set your schema once and serve different values and parameters to any user segment.

Experiments.Measure impact at the feature level, trust the statistics behind it, and run experiments wherever your data lives.

RBAC & Policies.Every change, human or agent-driven, is governed by the same approvals and OPA policies that manage production today.

Integrated AI Features

See the signal and measure the impact

Use prompts to explain experiment results, highlight metric impacts, and guide your next steps: rollout, rollback, or tuning. No more spreadsheet analysis or manual metric correlation.

Explain Results. AI analyzes your A/B tests and tells you what changed, by how much, and whether the result is statistically significant.

Impact Analysis. See the results of flag changes on performance metrics, conversion rates, and business KPIs. Understand impact in near real-time.

Recommendations. AI suggests when to roll out, scale back, or kill a feature based on its actual impact on your key metrics.

Built for every role

One tool for your whole team

Ship features, measure impact, and iterate fast

Launch features to specific segments without engineering tickets

See real-time impact on conversion, engagement, and revenue

Pause the rollout if key metrics trend in the wrong direction

Feature Flags · 4 active

new-checkout-flow● 10% rollout

recommended-products✓ Enabled

dark-mode-toggle● Beta users

payment-v2○ Disabled

AI monitoring all active flags in real-time

Customer stories

Trusted by engineering teams worldwide

Team confidence

“Harness has become a key part of our overall strategy. It increases the velocity of experimentation, strengthens our culture of safety, and helps us deliver better customer experiences every day.”

— Andrew Boellstorff, Director of Digital Product & Technology, Speedway Motors

ROI in weeks

“The benefit and the ROI that we have seen has now been 105% according to the proof of value we did with the Harness team.”

— Chris Davis, VP of Product Development, ADP

Data-driven decisions

“Harness helps us understand how users respond to changes and identify the best path forward.”

— Jean Steiner, VP of Data Science, Skillshare

FAQ

Frequently asked questions

Feature Management & Experimentation extends traditional feature flag capabilities to cover the full lifecycle of AI agents and application behavior. It lets teams control feature releases and AI agent rollouts using the same progressive delivery primitives: percentage-based targeting, kill switches, real-time config updates, and metric-driven decisions.

High-value use cases include AI configs and configs, and safely executing infrastructure migrations by validating new systems through dual reads, verifying they match legacy behavior, gradually ramping write traffic, and maintaining rollback capability throughout.

Feature flags separate the deployment of code from its release to users. They allow new features to be deployed but hidden, enabling controlled exposure via gradual rollouts or canary releases. If an issue is detected, the flag can be instantly turned off without requiring a full application redeployment.

Harness Worker Agents like FME's Feature Flag Cleanup Agent integrates directly into your pipelines to automatically detect stale flags based on rollout status, targeting rules, and inactivity metrics. Teams can run scheduled scan-only audits to build trust, then trigger cleanup workflows that generate individual draft pull requests for safe human review.

Harness Feature Flags lets you control features without deploying new code by decoupling deploy from release. SDKs integrate into your application code, allowing you to wrap features in flags that can be toggled on or off at runtime. Key differentiators include CI/CD pipeline integration, built-in governance with approvals and audit trails.

Release & experiment in production at the speed of AI

Your flag tool isn't a configuration platform.

Control every release, govern every config, measure every change.

Update AI behavior at runtime with built-in experimentation

Extends AI to the SDLC for configs, flags, and experiments.

See the signal and measure the impact

The only runtime management platform with governance built in.

Feature Flags

Configs

AI Configs

Experimentation

Warehouse Native

Segments

Release Monitoring

Harness AI

One tool for your whole team

Ship features, measure impact, and iterate fast

Trusted by engineering teams worldwide

We speak your language

Connect to your entire stack

Frequently asked questions

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