Loop engineering, an emerging architectural pattern for AI agent orchestration, recently gained empirical validation through a deterministic benchmark that isolates control-flow resilience from model capability. Conceptually structured by Google engineer Addy Osmani in mid-2026, the paradigm shifts development focus from prompt design to iterative system control. The pattern builds on earlier workflows by Peter Steinberger, Boris Cherny, and Geoffrey Huntley, which advocated for designing the operational loops that guide agents rather than writing direct instructions. Rather than relying on large language models for every decision, loop engineering structures workflows around observable state, dependency graphs, and goal-directed iteration. To verify the architecture without the confounding variable of model nondeterminism, a developer published a zero-dependency Python implementation that executes without a single LLM call. The system treats task orchestration as a directed acyclic graph, deploying a state-machine controller that continuously evaluates dependencies, resource availability, and execution outcomes. This design deliberately separates the control loop from the reasoning engine. A single swappable function handles task execution, meaning the architecture can operate on hardcoded rules or frontier models without altering the underlying flow or state management. The benchmark evaluated 300 randomized task configurations, injecting approximately 25 percent permanent blockers to simulate real-world pipeline fragility. A standard linear executor served as the baseline, halting entirely upon encountering its first unresolved dependency. The loop controller dynamically rerouted around blocked branches, continuing execution on independent, resolvable tasks. Rigorous sanity checks and regression testing validated the methodology, including a critical fix that replaced a binary resource lookup with a tri-state signal to prevent premature deadlock detection. These checks confirmed that iteration budgets, not heuristic guesses, determine when tasks are permanently stalled. Results demonstrate a clear architectural advantage. Across the tested configurations, the goal-directed controller completed an average of 3.3 out of 10.3 independent branches, compared to 0.4 for the linear baseline. The performance gap stems not from enhanced reasoning capability, but from failure isolation. While both systems inevitably stalled on permanently unresolved tasks, the controller converted catastrophic, all-or-nothing pipeline collapses into partial, localized interruptions. Performance variance across seeds confirms that while resilience is measurable and reproducible, absolute task completion remains constrained by underlying task feasibility and missing resources. This experimental validation reinforces loop engineering as a foundational pattern for building fault-tolerant AI workflows. By decoupling orchestration logic from model inference, developers can implement retry mechanisms, dependency tracking, and state persistence without baking specific reasoning strategies into the control layer. The complete implementation and benchmark scripts have been released publicly, offering practitioners a transparent baseline for evaluating control-flow architectures in agentic systems. As the field matures from prompt-centric prototyping to structured system design, this pattern provides a measurable framework for engineering pipelines that degrade gracefully under partial failure conditions.