system-scalability

Goal

To audit an existing architecture or propose a new one that appropriately utilizes horizontal and vertical scaling techniques, database sharding/partitioning, and decentralized state management to meet projected performance loads.

Steps

1. Current State Analysis: Evaluate the system's current boundaries (e.g., monolithic database, stateful web servers).
2. Dimension Scaling: Determine the optimal approach for the workload:
   • Vertical Scaling (Scale Up): Suggest hardware upgrades (CPU/RAM) where architectural changes are temporally or financially restrictive.
   • Horizontal Scaling (Scale Out): Propose adding more instances via Load Balancers, Kubernetes, or Auto-Scaling Groups. Ensure application state is externalized (e.g., Redis).
3. Database Scaling: Propose Read Replicas, Sharding (range vs. hash), Partitioning, or NoSQL migrations for data-heavy bottlenecks.
4. Documentation: Output the findings using agents/templates/architecture/SCALABILITY_ANALYSIS_TEMPLATE.md.

Security & Guardrails

1. Skill Security

• No Direct Infrastructure Muting: The agent must output Terraform/Kubernetes manifests for scaling but cannot execute apply operations directly against production clusters.

2. System Integration Security

• Data Locality in Sharding: When suggesting database sharding, the agent must verify that the sharding key respects any regional data privacy laws (e.g., EU data stays in EU shards).