A Crom is one unit of preventable AI workflow waste. It measures inefficiency across cost, latency, failure risk, and structural dimensions.

How are Croms calculated?

Croms are calculated by analyzing workflow graphs for cost waste, latency waste, failure risk, and structural inefficiency. Each issue is categorized by severity and mapped to optimization recommendations.

What are the four Croms dimensions?

Cost waste (overspending on models), Latency waste (sequential bottlenecks), Failure risk (missing error handling), and Structural inefficiency (redundant nodes, missing caching).

Croms™: AI Workflow Waste Metric — Cromus

Croms (Cromus Risk and Optimization Metrics) are the proprietary unit of measurement for preventable AI workflow waste. One Crom represents one unit of waste that can be identified and eliminated before execution. Croms are Step 2 in Cromus's 4-step measurement framework.

Croms measure waste across four dimensions: Cost waste (overspending on model selection, redundant API calls, unoptimized token usage), Latency waste (sequential execution of parallelizable tasks, unnecessary round-trips, unoptimized prompts), Failure risk (missing error handling, no retry logic, single points of failure, unvalidated outputs), and Structural inefficiency (redundant nodes, missing caching opportunities, suboptimal graph topology).

Each Crom is categorized by severity (critical, high, moderate, low) and mapped to specific optimization recommendations. The Croms score provides a workflow-level health metric that improves as recommendations are implemented.

Croms connect to the broader Cromus framework: Workflow Classification (Step 0) identifies where models are used, Baseline Cost per Workflow (Step 1) calculates current costs, Croms (Step 2) quantify waste, and TCWO (Step 3) provides the complete financial picture.