Signal Over Noise

This episode exposes the "Phantom Meters" of Agentic AI—the hidden, compounding token costs that standard observability dashboards fail to track. It breaks down critical architectural blind spots, including the reasoning tax of chain-of-thought models, massive token waste from raw tool result injections, context poisoning, and multimodal billing surprises. It also unpacks a real-world case study of a runaway agent loop that burned $47,000 in 11 days. It provides engineering teams with the architectural discipline needed to stop paying for the same thought twice. Listeners will walk away with actionable, framework-level strategies—such as implementing result-compression layers, setting explicit "thinking" budgets, and building per-task evaluation matrices—to achieve full-spectrum token visibility and dramatically reduce their production AI costs.