Execution layer boosts agent reliability to 70%

Expecting LLMs to generate text and manage execution logic simultaneously is a recipe for context drift and inevitable workflow failure. Traditional prompt-chaining frameworks often mask the complexity of state management until the entire system breaks down. Separating output generation from execution constraints allows even lightweight models to perform highly reliable multi-step tasks. This highlights a necessary shift from pure prompt engineering toward traditional systems engineering in AI application development.