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.

This work suggests a measurable, principled approach: treat runtime drift as entropy, and use it as a first-class signal in infrastructure systems.

Formal Proposal

Let a system’s intended configuration and behavior be defined via a set of known sources:

  • Declarative configurations (e.g., Git, Terraform, Kubernetes manifests)
  • Parameter stores and secrets managers
  • Known external interfaces

Let its actual runtime state be observed via:

  • Cloud infrastructure state (e.g., AWS API responses)
  • Logs, metrics, and traces
  • Network and application-level telemetry
  • Incident and cost data

The system constructs a probabilistic graph of expected behavior, then compares incoming signals against that model. The divergence between expectation and observation is quantified using Shannon entropy:

[ H(X) = -\sum p(x) \log_b p(x) ]

This metric becomes a direct signal of epistemic drift.

Implications

A system that continuously measures this drift can:

  • Identify and surface meaningful anomalies earlier
  • Correlate changes with emerging instabilities
  • Adapt control policies dynamically
  • Create a data trail for downstream learning and modeling

More broadly, this lays groundwork for self-evaluating systems — capable of detecting when they no longer “know” what they are doing, and responding accordingly.

  • Shannon’s 1948 work formalized uncertainty as information entropy.
  • Runtime policy engines like Open Policy Agent enforce compliance but do not quantify drift.
  • ML observability platforms monitor models post-deployment but lack generalizable metrics for infrastructure intent.

This proposal may bridge such gaps by grounding the notion of drift in a general information-theoretic framework.

Future Work

  • Define formal mappings from infrastructure state to probabilistic graphs
  • Determine how to establish and update baseline expectations
  • Explore visualizations, versioning, and diffing using entropy as distance
  • Investigate use in real-time alerting, auto-remediation, and governance

Conclusion

Treating runtime drift as entropy opens a path toward principled reasoning about system behavior. It suggests that infrastructure systems can become not only observable, but self-observing, with the ability to recognize — and eventually respond to — the loss of alignment with their own intentions.

This post is a first stake in the ground. Further exploration will occur via ongoing work on Adage and Karma.

For questions or collaboration: https://strall.com