Multi-agent LLM systems promise autonomous task execution but suffer from twocritical reliability failures: false positives (systems report success on tasks that failstrict validation) and retry inflation (traditional retry logic degrades performance whileincreasing costs). We demonstrate that standard validation approaches produce a 66%false positive rate on structured extraction tasks, with naive prompting achieving 100%reported success but only 34% true success under universal validation criteria. We presentManifold, a specification-driven orchestration architecture that treats specificationsas verifiable contracts between agents. Manifold combines the Specification Patternfrom object-oriented design with fingerprint-based loop detection to prevent infiniteretry cycles while ensuring output correctness. Across 600 controlled trials spanningfour domains (adversarial image generation, structured data extraction, and multi-stepsynthesis), Manifold achieved 94% true success rate versus 34% for naive prompting(p < 0.001, Cohen’s h = 1.40) with zero false positives compared to naive’s 66%false positive rate. On structured extraction tasks, Manifold produced 99.1% field-levelaccuracy while eliminating all false positives. Smart control (retry logic) degraded to58–98% success rates while inflating costs by 1.5–3.5× across all experiments. Ourresults demonstrate that specification-driven validation enables trustworthy autonomousoperation by providing verifiable correctness guarantees, with implications for productionLLM deployment at enterprise scale.