Wix Engineering proposes a Feedback-Driven Agent (FDA) architectural pattern to address AI agent reliability problems caused by context fatigue and oversized prompts. The core idea is separating structural integrity enforcement (via deterministic validators) from goal-based assessment. The FDA loop has the agent draft output, run it through a programmatic validation engine, and receive targeted error reports with specific repair instructions — no second LLM judge required. Three real-world implementations are described: the Jay Framework for frontend guardrailing with design token and accessibility validators, a WebMCP plugin that lazy-loads tool schemas only when an agent makes a naive call, and self-validating APIs that return structured HTTP 400 responses with inline schemas and remediation links. The key insight is that building resilient feedback infrastructure — not bigger prompts — is what makes agents reliably self-correcting.
