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AFIE Documentation

AFIE — Autonomous FinOps Intelligence Engine for Kubernetes. A closed-loop system that scrapes cluster telemetry, learns a resource-adjustment policy with reinforcement learning, validates each recommendation against a policy engine, and applies changes through GitOps pull requests.

This directory contains the engineering documentation. The IEEE paper drafts live under ../paper/; the phase-by-phase implementation workflow lives in AFIE_Workflow_updated.docx at the repo owner's desktop.

Start here

Doc What it covers
architecture.md System overview, components, data flow, tech stack
telemetry-service.md Phase 3 service — Prometheus scraping, MetricEvent JSONL sink
feature-engineering-service.md Phase 4 service — sliding window, 47-dim state vector, /state endpoint, Postgres persistence
infrastructure.md KIND cluster, Prometheus/Grafana, ArgoCD, Postgres
development.md Build, test, and run locally
roadmap.md Planned scope for Phases 5–9
doc.md Local dev credentials cheatsheet

Current status

Repository layout

src/
  api/
    telemetry/           Phase 3 — ASP.NET Core scraper (built)
    feature-engineering/ Phase 4 — 47-dim state vector + Postgres (built)
    bff/                 Phase 7 — scaffold only
  operator/              Phase 6 — scaffold only
  ml/                    Phase 5 — scaffold only
  dashboard/             Phase 7 — scaffold only
  shared/
    AFIE.Contracts/      Shared DTOs (MetricEvent)
infra/
  gitops/                ArgoCD apps, CRDs, plain manifests
  terraform/             Phase 8 — Azure IaC (scaffold)
  benchmarks/            Phase 9 — experiment workloads
tests/
  AFIE.Telemetry.Tests/            xUnit tests for Phase 3
  AFIE.FeatureEngineering.Tests/   xUnit tests for Phase 4
  AFIE.Operator.Tests/             Phase 6 — scaffold only
experiments/             Local telemetry sinks + Phase 9 analysis
scripts/                 Phase 2 bootstrap
paper/                   IEEE draft

About

Autonomous FinOps Intelligence Engine (AFIE) — An AI-powered Kubernetes FinOps platform that uses reinforcement learning, policy-as-code, and GitOps to automate safe cloud resource optimization.

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