Observability

Entropy-Augmented DAG Observability unifies flows, telemetry, and governance into a predictive system that prevents failures before they happen.

A technical deep dive into entropy-augmented DAG observability: Markov chains, Bayesian inference, and field-theoretic analogies with Ampère–Maxwell law.

Extend the MongoDB → Kafka → ClickHouse pipeline with ServiceNow ticket data to provide early-warning signals for incidents, helping on-call engineers see problems before tickets are created.

ClickHouse schema definitions and example queries for the TicketSoon pilot, integrating MongoDB CDC, system events, and ServiceNow tickets into a unified event store.

Abstract This document outlines a foundational perspective for a possible future discipline of runtime epistemology — the study of how infrastructure systems can quantify their own state of divergence from intended behavior. It proposes that Shannon entropy offers a mathematically principled basis for measuring runtime drift in live systems, forming the core of a design pattern suitable for both operational reliability and machine-driven reasoning. Introduction Contemporary infrastructure systems are increasingly dynamic, distributed, and subject to change. While observability tools have improved, systems still rely on humans to reconcile what is happening with what was supposed to happen. This epistemic gap — the difference between actual and intended behavior — remains largely qualitative, ad hoc, and unmeasured. ...