Agentic AI systems can now generate code with remarkable fluency, but a fundamental question remains: emph{does the generated code actually do what the user intended?} The gap between informal natural language requirements and precise program behavior — the emph{intent gap} — has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale. This article argues that textbf{intent formalization} — the translation of informal user intent into a set of checkable formal specifications — is the key challenge that will determine whether AI makes software more reliable or merely more abundant. Intent formalization offers a tradeoff spectrum suitable to the reliability needs of different contexts: from lightweight tests that disambiguate likely misinterpretations, through full functional specifications for formal verification, to domain-specific languages from which correct code is synthesized automatically. The central bottleneck is emph{validating specifications}: since there is no oracle for specification correctness other than the user, we need semi-automated metrics that can assess specification quality with or without code, through lightweight user interaction and proxy artifacts such as tests. We survey early research that demonstrates the emph{potential} of this approach: interactive test-driven formalization that improves program correctness, AI-generated postconditions that catch real-world bugs missed by prior methods, and end-to-end verified pipelines that produce provably correct code from informal specifications. We outline the open research challenges — scaling beyond benchmarks, achieving compositionality over changes, metrics for validating specifications, handling rich logics, designing human-AI specification interactions — that define a research agenda spanning AI, programming languages, formal methods, and human-computer interaction.<\/p>\n","protected":false},"excerpt":{"rendered":"
Agentic AI systems can now generate code with remarkable fluency, but a fundamental question remains: emph{does the generated code actually do what the user intended?} The gap between informal natural language requirements and precise program behavior — the emph{intent gap} — has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale. 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