Taming LLMs in Production: Control Patterns for Coding Agents

Contributed by Ritik Jain

Production LLMs can look impressive in demos, but in the real world they often fail in repeatable ways: infinite loops, runaway edits (“crazy cursor”), tool-chain fragility, hallucinations, and inconsistent clinical summaries. This whitepaper argues that reliability doesn’t come from “smarter prompts,” but from engineering a control stack around the model. It lays out practical patterns, budgets, disciplined state/context management, schema validation, reflection/critics, routing, and safe fallbacks, to make LLM systems controllable and auditable. Along with playbooks and checklists, it shows measurable gains like reduced loop rates and higher consistency for clinical workflows.