Open source Adage: Configuration-Driven AWS Deployment Framework wasn’t built as a machine learning platform — but it enables one. Because each deployed component exposes its runtime state, dependencies, and configuration via AWS Parameter Store, the system becomes:
- Observable
- Configurable at runtime
- Adaptable without redeployment
What Is Adaptive Runtime Behavior?
In this context, it means infrastructure that responds to runtime conditions:
- Services that select behavior based on observed state
- Configuration that adjusts over time
- Decision logic that operates independently from deployment processes
Why This Framework Supports It
1. Decoupled Runtime State
Every component stores its runtime info under a consistent path:
/iac/<component>/<nickname>/runtime
This makes it easy to:
- Query current system state
- Compare it to intended config
- Support external logic without needing to reapply Terraform
2. Configuration as Control Layer
Because infrastructure behavior is defined in JSON config, it’s possible to:
- Track and reason about changes
- Apply decisions dynamically
- Swap rule-based logic for model-based logic incrementally
Configuration can evolve independently of infrastructure modules.
3. External Decision Logic
Runtime and config data are both accessible without touching infrastructure code. This allows external systems to:
- Recommend or modify config
- Generate runtime adjustments
- Surface decisions to operators or apply them directly
This is compatible with both human-in-the-loop and automated workflows.
Potential Use Cases
This approach makes it feasible to build systems like:
Adaptive Routing
A service selects a database instance based on runtime latency metrics.
Config-Aware Scheduling
A batch job sets its own concurrency based on historical job durations.
Deployment Risk Checks
A plan validator compares proposed config changes against prior patterns and flags high-risk diffs.
Traffic Control
Traffic shifting between two versions is based on observed success rates.
These patterns don’t require deep ML integration to be useful — they rely on accessible system data.
Human Oversight Remains Central
The framework is compatible with ML-driven adaptation, but it doesn’t require it. Configuration stays in Git. Runtime logic can be monitored or paused. Operators remain in control.
This supports workflows such as:
- Training models offline using structured config + runtime + cost data
- Surfacing config recommendations as pull requests or UI prompts
- Gradually increasing automation as confidence grows
Summary
This infrastructure model supports adaptation by design. It separates configuration from infrastructure, makes runtime behavior accessible, and provides clear integration points for automation.
Systems that externalize configuration and expose runtime state are easier to reason about — and easier to evolve.
For more context, see the Adage Design Principles →