An engineer tackles recurring LLM failures by adding a Control Layer above the model. The layer catches broken JSON, injection attempts, and provider outages, turning a 0% structured‑output pass rate into 100% without altering prompts.

Initial crashes stemmed from malformed JSON, hidden system‑prompt leaks, and silent backend downtimes. Repeated prompt tweaks failed; the root issue lay in architectural gaps that let unsafe or incomplete data reach the application.

The eight‑component stack—InputGuard, TokenBudget, PromptBuilder, ResponseValidator, CircuitBreaker, RetryEngine, FallbackRouter, and AuditLogger—runs on Python 3.12.6 under Windows 11. Benchmarks show the naive setup fails every test, while the full stack passes all 69 tests and demo queries.

Deploying this layer makes LLM‑driven pipelines safe, cost‑efficient, and reliable. The system validates input, enforces token limits, and safeguards downstream logic, proving essential for any production service that relies on structured LLM output.