# archAIc Reliability Engineering Platform

> Distributed reliability engineering platform with observability, metrics, tracing, and anomaly detection.

Page: https://adith.xyz/projects/archaic
Author: [Adith Manikonda](https://adith.xyz/about.md) (https://adith.xyz)
Last updated: 2026-10-21

- Built by: Adith Manikonda
- Year: 2026
- Category: Systems / Backend
- Built with: Kubernetes, OpenTelemetry, Grafana, Python, Observability
- Source code: https://github.com/Ad1th/archAIc

## The problem

Observability tooling is almost always demonstrated against a healthy system, which tells you nothing about whether it catches the failures you actually get at 3am.

## The approach

Four instrumented services with a real dependency graph, where product and payment both depend on auth and db, so a single degraded dependency produces a genuine cascade rather than an isolated error. Failures are injected probabilistically, time-bound and intensity-scaled, so the same experiment can be repeated. An Isolation Forest detector proxies Prometheus metrics and webhooks an operator service that attempts recovery. A shared trace_id follows a request across every hop, which is what makes root-cause attribution possible at all.

## Measured

- Instrumented services: **4** (auth, db, product, payment, plus detector and operator)
- Load profiles: **4** (k6: normal, spike, endurance, stress)

## What it does not do

Recovery is executed by an LLM operator rather than a deterministic runbook, so its actions are suggestions with side effects, not guarantees. The chaos layer and the detector are the parts that hold up under repetition.

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