The Theory Behind Karma #

Infrastructure as Consequence means that what you deploy is not just the result of a plan — it's the outcome of everything that came before.

Karma is built on the idea that infrastructure can be:

• Described as a graph of interconnected objects
• Shaped by lineage, not static templates
• Modified intentionally, not drifted silently
• Observed and re-evaluated continuously, not just provisioned once

Graph-Based Thinking #

Karma encourages you to model infrastructure as a runtime graph, where:

• Each node represents a declarative component
• Edges define runtime dependencies — not file-level includes
• Relationships are stored and queried in Amazon Neptune

This makes it easier to:

• Visualize how infrastructure is composed
• Understand the blast radius of a change
• Propagate updates intentionally, based on real dependencies
• Reroute or override behavior in specific contexts (e.g., QA vs. PROD)

Consequence, Not Just State #

Traditional Infrastructure as Code (IaC) tools model desired state and reconcile toward it.

Karma introduces the idea of consequence — not just “what should exist”, but “how did we get here?”

• Every deployment has context: what changed, who triggered it, what dependencies it resolves
• This enables safer rollback, more meaningful testing, and auditable infrastructure history
• Each component is self-describing and testable, with traceable lineage and reproducible behavior

The Object-Oriented Layer #

Each Karma component behaves like a smart object:

• It has inputs and outputs
• It can reference other components by nickname
• It can inherit behavior or override configuration in context
• It exists as a node in a graph, not a chunk of YAML or JSON

This allows for reuse, composability, and environment-specific behavior without duplication.

Learn More #

Read how Karma’s architecture enables modeling, simulation, and intelligent automation in
Data Science Implications →