Engineering design and quality review often require evidence from technical standards issued by multiple organizations. In a limited context window, partial evidence is often included, and relevance-based retrieval results can become biased toward sources with a large volume of documents, leaving other necessary sources unrepresented. We implemented a deterministic source-aware retrieval layer that identifies required source groups, retrieves candidates through source-conditioned channels, and prioritizes one candidate from each channel. We also evaluated a configuration that uses within-channel cross-encoder reranking to select that candidate. Evaluation used 15 technical standards, 72 evaluation questions, and 162 annotated evidence locations, with k set to 5 for all methods. Required-source coverage was 61.5% for dense retrieval and 78.6% for cross-encoder reranking, whereas both source-aware methods achieved 100%. Source-aware reranked channel quota achieved an evidence recall of 46.0% and retrieved all required evidence for 27.8% of questions, compared with 41.8% and 23.6% for cross-encoder reranking and 32.1% and 15.3% for dense retrieval. Within the source-aware design, within-channel reranking increased mean reciprocal rank from 0.445 to 0.545. These results support separating required-source coverage from evidence ranking in bounded-context retrieval for domain-specific agent memory.