AWS targets AI slop with new spec check in Kiro coding tool, amid scrutiny of agent reliability

Amazon Web Services is adding a feature to its Kiro AI coding tool designed to mathematically prove that software requirements are free of contradictions and gaps before any code gets written, addressing one of the core risks of AI-assisted software development. The feature, called Requirements Analysis, is designed to catch the kind of bugs that can often be the hardest to spot and most expensive to fix — problems that start not in the resulting code but in the initial requirements that define what the software is supposed to do. The announcement Tuesday morning comes three months after Amazon publicly pushed back on a Financial Times report that its AI coding tools contributed to AWS outages, an episode that highlighted the risks of giving AI agents too much autonomy in software development. It also comes a day after AWS hired former Microsoft exec Shawn Bice to return to Amazon as VP of AI Services leading its Automated Reasoning Group, the team behind the new feature. Bice will report to Swami Sivasubramanian, Amazon’s VP of Agentic AI. Requirements Analysis combines large language models with an automated reasoning engine called an SMT solver. The LLM translates natural-language requirements into formal logic. The solver then checks those requirements by mathematically proving whether they contradict each other or leave gaps that could be filled in erroneously by the AI coding tool — a common problem as AI increasingly generates software faster than developers can review it. “Every vague prompt produces a vague spec or plan, and the AI agent implementing that spec produces code full of undisclosed decisions made on your behalf, without your awareness or agreement,” wrote AWS applied scientists in a blog post accompanying the news. Kiro competes in a crowded and fast-growing market for AI coding tools that includes Cursor, GitHub Copilot, Anthropic’s Claude Code, Google’s Antigravity, and OpenAI’s Codex. While those tools have increasingly added planning and agent workflows alongside code generation, Kiro has built its identity around a spec-first approach that requires developers to formalize their intent before the AI starts building. AWS also announced two other Kiro features designed to speed up the development process. Parallel Task Execution runs independent coding tasks concurrently rather than sequentially, cutting implementation times for large projects by roughly 75 percent, according to the company. AWS says a new Quick Plan mode lets developers skip the step-by-step approval process for well-understood features, generating a full set of requirements, design, and tasks in one pass. PREVIOUSLY: Amazon’s surprise indie hit: Kiro launches broadly in bid to reshape AI-powered software development