FeatBitFeatBit
AI Release Engineering

Extended Pillar

AI-NativeDevOps Stack

A DevOps stack designed for AI needs more than CI/CD and monitoring. It needs runtime release control, behavioral observability, and continuous experimentation — integrated from the start. That is why FeatBit is positioned as the FeatureOps control plane for the AI era.

Deploy FeatBit Free See: AI Experimentation
“Traditional DevOps was built to ship code safely. AI DevOps must also ship behavior safely — and those are different problems with different toolchains.”

What Changes in an AI-Native Stack

Traditional

Code deployment = behavior change

AI-Native

Behavior changes independent of deployments

Traditional

Monitoring checks error rates

AI-Native

Observability tracks behavioral quality

Traditional

Tests validate deterministic outputs

AI-Native

Experiments measure probabilistic quality

The Five-Layer
AI DevOps Architecture

Instrumentation Layer

FeatBit Skills, OpenTelemetry SDKs

Automatically instruments AI decision points with feature flags and emits OTel traces from every flag evaluation. FeatBit Skills enables coding agents to wire this layer without manual instrumentation.

Release Control Layer

FeatBit (core)

Owns feature flag state, user targeting, percentage rollout, and kill switches. This is where human operators and autonomous agents control what AI behavior is active for which users at any instant.

Observability Layer

OpenTelemetry Collector, Prometheus, Grafana, Jaeger

Collects and correlates OTel traces from flag evaluations with system metrics. Provides the causal chain from flag state to observed behavior — latency, error rate, output quality, downstream conversion.

Experimentation Layer

FeatBit (experimentation), statistical backends

Runs A/B tests and multi-variant experiments on AI features. Experiments are controlled by flags, measured through OTel events, and concluded with statistical significance gates that trigger automatic rollout or rollback.

Orchestration Layer

CI/CD pipelines, agent workflow systems

Deploys code artifacts and triggers flag changes on completion. In AI-native teams, coding agents participate in this layer — using FeatBit Skills to instrument new decision points as part of every feature branch.

FeatBit in the AI DevOps Stack

FeatBit occupies two layers simultaneously: it is the release control layer (feature flags, percentage rollouts, kill switches) and it is the instrumentation mechanism that generates OTel events feeding the observability layer.

FeatBit Skills extends this into the orchestration layer — coding agents running in CI/CD pipelines or agent workflow systems can call FeatBit Skills to automatically instrument new AI decision points as they are added to the codebase.

The result: a DevOps stack where every AI feature is observable by default, controllable without redeployment, and instrumentable without manual effort.

Pipeline-Native AI Deployment Infrastructure

Feature Flags as First-Class CI/CD Citizens

Your AI pipeline doesn't need a dashboard. FeatBit CLI commands run inside any GitHub Actions step or GitLab CI stage — flags created, rollouts advanced, and rollbacks triggered without ever leaving the pipeline.

Skills: Instrument Pipeline Stages

Skills add feature flag gates to CI/CD pipeline code automatically — staging gates, canary triggers, and rollback conditions appear as flags without manual instrumentation.

GitHub Actions & GitLab CI Native

featbit flags create and featbit flags update run as shell commands in any pipeline step. No SDK, no config file, no dashboard login — just CLI calls in your YAML.

No Pipeline Overhead

Flag creation and evaluation calls complete in sub-millisecond time. Your AI deployment pipeline doesn't slow down for a feature flag gate.

Full Deployment Audit Trail

Every flag created or updated in a pipeline run is attributed to the commit SHA, pipeline ID, and runner. Your deployment history is a native flag audit log.

Self-Hosted for Isolated Pipelines

FeatBit runs inside your build network. No egress to external SaaS. Works in air-gapped CI environments and regulated deployment zones where external calls are prohibited.

deploy.yml (GitHub Actions)
# GitHub Actions: create deployment flag gate before rolling out
# $GITHUB_SHA is injected automatically by GitHub Actions
- name: Create deployment flag
  run: |
    featbit flags create --key "deploy-$GITHUB_SHA" \
      --type boolean --env staging

# Post-deploy: agent smoke-tests and advances rollout automatically
- name: Progressive rollout
  run: |
    for PCT in 5 25 100; do
      featbit flags update "deploy-$GITHUB_SHA" --rollout $PCT
      sleep 300
      ERR=$(featbit metrics get error-rate --flag "deploy-$GITHUB_SHA" --last 5m)
      (( $(echo "$ERR > 1.0" | bc -l) )) && { \
        featbit audit log "pipeline-rollback: sha=$GITHUB_SHA err=$ERR"; exit 1; }
    done

Add Release Control to Your AI DevOps Stack

FeatBit integrates into any AI DevOps stack as the release control layer — open source, self-hostable, deployable in five minutes, and compatible with every observability and CI/CD tool.

Deploy FeatBit Free See: AI Experimentation