AWS found bugs in 60% of software requirements. Its fix isn’t more AI — it’s a 50-year-old logic engine.
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AWS has added a Requirements Analysis feature to its Kiro agentic development platform that combines LLMs with SMT (satisfiability modulo theories) solvers — a formal logic engine with roots in the 1970s — to catch contradictions, ambiguities, and gaps in software specifications before they reach code. In internal testing across 35 projects with over 1,400 acceptance criteria, roughly 60% of first-draft requirements needed refinement. The three-stage pipeline rewrites natural-language requirements into precise criteria, translates them into formal mathematical logic, then runs proofs to surface issues as plain-language questions developers can resolve in 10–15 seconds each. AWS frames this as neurosymbolic AI — pairing neural network pattern-matching with symbolic logic's mathematical certainty — arguing that speed without correctness just means writing wrong software faster. Additional new Kiro features include Parallel Task Execution (cutting large-spec implementation time by ~75%) and Quick Plan. Early adopters include Nymbus, Delta Air Lines, Nielsen, Siemens, and Amazon's own internal teams.
