Requirements-to-code traceability supports essential software engineering activities, including change impact analysis, maintenance, and regulatory compliance. Existing approaches typically rely on a retrieval-and-classification pipeline, which suffers from an inherent retrieval bottleneck: relevant source files that are not retrieved cannot be recovered during subsequent analysis. In this paper, we revisit requirements-to-code traceability in light of the substantially larger context windows offered by modern large language models, which make repository-scale analysis feasible without a separate retrieval step. We propose three approaches: a single-agent approach that analyzes the entire code repository in one pass, and two multi-agent approaches based on self-verification and multi-agent debate. Our evaluation shows that the single-agent approach substantially improves recall, achieving 80.9%, a gain of 38.6 percentage points (pp) over the state-of-the-art retrieval-based baseline, albeit with a 25.8 pp reduction in precision. By leveraging collaborative reasoning, the multi-agent debate-based approach achieves the best overall precision-recall balance, with 24.5% precision and 71.0% recall, yielding an F2 score of 49.5%. This represents an improvement of 7.5 pp over the state of the art and 2.7 pp over the single-agent approach. While the quantitative evaluation establishes the overall effectiveness of the approaches, the aggregate metrics alone explain little due to the considerable variability across results. Importantly, qualitative analysis provides key insights into highly variable quantitative results: the retrieval approach suits requirements that echo the code's vocabulary; single-agent, full-context reasoning suits well-documented, consistently named code; and multi-agent debate suits indirect, conflicting, or distributed evidence.

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