Operations
FinOps frameworks and hardware repatriation metrics to manage spot-GPU usage, local compute breakevens, and token decay.
Observed Evidence
The Direct Experience
"During cloud infrastructure assessments, I watched a startup burn $40K in a single weekend because a multi-agent loop entered an infinite retry cycle. The pattern was highly repetitive: teams deployed autonomous agents with direct API access without setting loop caps or cost circuit breakers. The mechanism is Token Inflation—agents query models in loops, expanding context windows and scaling billing exponentially. This illustrates the principle of unoptimized inference routing: uncontrolled agent loops lead to rapid capital erosion. The broader implication is that companies must deploy autonomous agents behind sovereign gateway proxies that enforce deterministic token and cost budgets."
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
Agentic FTE Simulator
A model mapping the headcount substitution rate and margin adjustments of deploying AI agents in support and operations.
Organizations deploy customer support bots without calculating the real cost of handling edge cases (human intervention rates), distorting savings projections.
Calculates the real EBITDA payback of automation projects.
Cloud Repatriation Breakeven
The financial math calculating the exit point from cloud databases (AWS, Azure) to private bare-metal configurations.
Companies stay on hyperscalers for high-velocity database access, bleeding up to 60% of operating cash flow on markup pricing.
Recaptures lost cash flow to directly improve EBITDA margins in mature products.
Token Simulation
A model calculating prompt expansion and context decay in multi-step reasoning chains.
Multi-agent workflows run recursive loops that expand prompt sizing exponentially, ballooning execution bills in hours.
Sets clear guardrails to avoid runaway looping bills.
Want to apply this to your organization?
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Richard Ewing — AI Economist & Capital Auditor