The Loop Is the Product — Roland Gavrilescu, Introspection

Autonomous AI agents require a shift from static code to continuous, self-improving loops that mirror OODA (Observe, Orient, Decide, Act) processes. By treating the loop as the product, developers can build long-running agents that learn and adapt within their environment. System distillation serves as the primary methodology, where human judgment—or "taste"—is codified into portable, versioned agent recipes. These recipes enable the reproduction of frontier-level AI performance across diverse platforms. Success is measured by "valued work per watt," ensuring that the economic cost of agent execution aligns with the quality of the output. Using examples like OpenClaw, which automated complex negotiations, this approach emphasizes that agents must not only execute tasks but also calibrate themselves to the creator's intent, creating a defensible, vertical AI strategy that evolves through iterative production testing and constant refinement of evals.