Efficient Context Retrieval for Data-Centric Agents over Heterogeneous Sources

Data-centric agents increasingly operate over heterogeneous environments that combine structured data with operational APIs. In such settings, retrieving the small subset of catalog entities relevant to a user query is often more challenging than executing the resulting tool calls. The DASHSys challenge exemplifies this problem through a relational knowledge graph and hundreds of REST endpoints exposed by Adobe Experience Platform and Adobe Journey Optimizer. We present a modular architecture that separates context retrieval from agent execution through a structured metadata interface. The retrieval stage combines factual catalog extraction, semantic augmentation of schemas and OpenAPI specifications, and multiple retrieval strategies ranging from deterministic lexical and embedding-based methods to hierarchical LLM-based retrieval. The execution stage consumes the retrieved context through a ReAct-style agent equipped with SQL and API tools. Our evaluation on a curated benchmark of 100 verified queries shows that semantic augmentation provides larger gains than changes to the underlying retrieval algorithm, improving dimension-table recall from 74.6% to 94.9% and bridge-table recall from 25.0% to 93.8% for BM25F retrieval. Furthermore, a lightweight OSS retrieval pipeline that combines deterministic pre-selection with a single LLM reranking step approaches the retrieval quality of a substantially more expensive hierarchical LLM retriever while reducing average input tokens by 66%. The results suggest that carefully constructed retrieval context is sufficient to recover most of the benefits of full-catalog LLM retrieval. At the same time, exact grounding of tables, columns, endpoints, and parameters remains a key challenge for reliable data-centric agents.