Cost-Accuracy Tradeoff
Pure agentic AI systems send every query to frontier reasoning models. This delivers high accuracy but costs 10-100x more than necessary for routine decisions.
The Current State
Organizations deploying AI agents face a stark tradeoff. Frontier models like Claude 4.5 Opus and GPT-5.2 deliver exceptional reasoning but cost $15-75 per million tokens. For high-volume applications—customer service, document processing, claims handling—these costs become prohibitive.
The naive solution is to use cheaper models everywhere. But cheaper models fail on complex decisions, creating compliance risks and customer dissatisfaction. Organizations are forced to choose between cost and quality.
Not all decisions are equal. A password reset doesn't need the same reasoning as a fraud investigation.
The Insight
Decisions have different stakes—the potential impact of an error. A routine FAQ response has low stakes; a credit denial has high stakes. Trust Cascade routes each decision to the minimum processing tier capable of handling it correctly, based on these stakes.
Pure Agentic Approach
Every query to frontier models. High accuracy, unsustainable costs. $0.15-0.50 per interaction.
Trust Cascade
Stakes-based routing. Same accuracy where it matters, 86% cost reduction. $0.02-0.07 per interaction.
Cost Comparison
| Approach | Avg. Cost/Query | Accuracy | Latency |
|---|---|---|---|
| Pure Agentic (Frontier Only) | $0.35 | 97% | 3-8s |
| Pure Heuristics | $0.001 | 62% | <100ms |
| Cheap Models Only | $0.02 | 78% | 500ms |
| Trust Cascade | $0.05 | 94% | 200ms avg |