Structured output from agents is easy to generate and hard to trust. Field types, nesting, formatting, interaction rules, and semantic expectations all have to line up—and when they don't, the failure modes are grim: silent degradation, an engineer patching it after the fact, or a customer seeing the breakage. None of that is acceptable once agents sit in real execution paths.

Sharper prompts and richer templates do not solve the problem. The answer is to close the loop—expose validation that matches actual system constraints, return violations in a form the model can act on, and let the agent revise until the output satisfies the rules or a defined fallback takes over. It moves agents from one-shot probabilistic generation toward constraint-satisfying systems that converge without a human in the execution loop.

That pattern is agentic self-correction.

The heart of agentic self-correction is a validation loop: generate, validate against a programmatic authoritative source, receive structured feedback on what failed, where, and why, and correct using that signal. Repeat until the output is valid or a maximum-attempts ceiling triggers predictable fallback behavior. The same shape applies wherever outputs must adhere to an acceptance criteria: UI definitions, configs, code, workflows, diagrams, and other structured artifacts.

Structural validity is only the first bar. Self-correction can extend into experience quality. Experiential constraints—readability, information density, interaction clarity, cognitive load, task alignment—sit alongside structural ones such as schema correctness, formatting validity, and required fields. The extended loop is generate, validate, evaluate, correct, and repeat. Validation enforces correctness; evaluation guides improvement. Shifting from strictly correcting invalid outputs to optimizing them as well.