The primary constraint in AI-assisted software development is rarely the sheer capacity of modern foundation models to write syntactically valid code. Instead, the wall engineering teams hit when scaling code generation is the failure of single-shot prompts to handle complex, state-dependent systems. When an engineer relies on a single generation pass—no matter how detailed the prompt—the model operates in an open loop. It lacks situational awareness of execution results, dependency conflicts, or edge-case runtime behaviours. Scaling agent output requires moving from static generation to loop engineering: the deliberate design of closed-loop execution and feedback harnesses wrapped around stochastic model outputs. The Mechanics of Closed-Loop Architecture Loop engineering shifts the agent’s task from "write this code" to "converge on a passing state within defined bounds." Rather than serving as an end-to-end author, the agent becomes an operator inside a deterministic feedback environment. This cycle relies on three distinct feedback channels: Deterministic Verification: The agent's output is immediately evaluated inside an isolated runtime sandbox using static analyzers, type checkers (e.g., TypeScript compiler, MyPy), and linter rules. Contextual Reflection: When an execution error or test failure occurs, the standard error (stderr) and stack traces are formatted directly into the agent’s memory buffer as contextual constraints for its next attempt. Bounded Iteration Budgets: To avoid infinite loops and unbounded token consumption, the loop enforces a maximum cycle budget (e.g., $N=3$ iterations) alongside cost controls. Single-Pass Generation vs. Loop-Engineered Execution Consider the difference in outcome when assigning an agent to refactor a high-concurrency API service: Single-Pass Pipeline: The model generates 300 lines of refactored code. The syntax is clean, but it introduces a subtle race condition under heavy load. The pull request