Stop Building Single-Turn RAG: The Architectural Fix for Hybrid Data Queries

Why Your RAG Pipeline Cracks on Hybrid Queries

Your retrieval-augmented generation pipeline works fine until it doesn’t. It handles straightforward questions well—semantic search over documentation, basic question-answering over a knowledge base. But the moment a query requires joining data from multiple sources with different structures, the system breaks. This is not a model weakness. This is an architectural failure.

The Failure Mode You Are Probably Seeing

Consider a query that seems reasonable: “Which of our products have had declining sales over the past three months, and what potentially related issues are brought up in customer reviews on various seller sites?”

The sales data lives in your SQL data warehouse. The review sentiment lives in unstructured documents scattered across seller sites. A single-turn RAG system receives one query and issues one retrieval call. It cannot split that question, route each half to the appropriate data source, and combine the results. It lacks the mechanism to reason across data boundaries.

This is the specific failure mode Databricks’ research identifies. The company’s AI team tested multi-step agentic approaches against state-of-the-art single-turn RAG baselines across nine enterprise knowledge tasks. The results were consistent: gains of 20% or more on Stanford’s STaRK benchmark suite, with similar improvement across Databricks’ own KARLBench evaluation framework.

Start Using Multi-Step Agent Architecture for Hybrid Data

Stop trying to solve this with a better model. Databricks confirmed exactly that by rerunning published STaRK baselines using a current state-of-the-art foundation model. The stronger model still lost to the multi-step agent by 21% on the academic domain and 38% on the biomedical domain. The architectural gap dwarfs the model quality gap.

If your queries span structured and unstructured data, you need a different architecture. Here is what that architecture looks like in practice.

Parallel Tool Decomposition — Fire SQL and Vector Search Together

Single-turn RAG issues one broad query and hopes the results cover both structured and unstructured needs. Multi-step agents do not work that way. Instead, the agent fires SQL queries and vector search calls simultaneously, then analyzes the combined results before deciding what to do next.

This parallel step is what allows the system to handle queries that cross data type boundaries without requiring you to normalize your data first. The agent decomposes the question into a SQL query and a search query out of the box, combines the results, and reasons about the full picture.

Self-Correction — Let the Agent Retry With a Different Path

Retrieval fails. That is not a bug; it is a feature your architecture should handle. When an initial retrieval attempt hits a dead end, the multi-step agent detects the failure, reformulates the query, and tries a different path.

On a STaRK benchmark task requiring finding a paper by an author with exactly 115 prior publications on a specific topic, the agent first queries both SQL and vector search in parallel. When the two result sets show no overlap, it adapts and issues a SQL JOIN across both constraints, then calls the vector search system to verify the result before returning the answer. The agent reasons about whether the final answer was actually found—and if not, it keeps trying.

Stop Converting Your Data — Start Using Declarative Configuration

Custom RAG pipelines require data in a format the retrieval system can read. Text chunks with embeddings. SQL tables flattened into text. JSON normalized into strings. Every new data source you add means more conversion work, more preprocessing pipelines, more maintenance burden.

Databricks’ research argues this burden makes custom pipelines increasingly impractical as enterprise data grows to include more source types. The alternative is declarative configuration.

What Declarative Configuration Means in Practice

Connecting a new data source to a declarative agent framework means writing a plain-language description of what that source contains and what kinds of questions it should answer. No custom code is required. The agent handles routing and orchestration without additional engineering effort.

The practical consequence is significant. Across all tested deployments in Databricks’ research, the only things that differed were instructions and tool descriptions. The agent handled the rest. You bring the agent to the data, give it more sources, and it learns to use them.

Reassess Whether You Need a Custom RAG Pipeline at All

If your queries span structured and unstructured data, building custom retrieval is the harder path. The research is clear: the performance gap between single-turn RAG and multi-step agents on hybrid data tasks is an architectural problem, not a model quality problem.

For data engineers evaluating whether to build custom RAG pipelines or adopt a declarative agent framework, the direction is clear. The practical limits are real but manageable. The approach works well with five to ten data sources. Adding too many at once, without curating which sources are complementary rather than contradictory, makes the agent slower and less reliable.

Scale incrementally and verify results at each step rather than connecting all available data upfront. Data accuracy is a prerequisite—the agent can query across mismatched formats, JSON review feeds alongside SQL sales tables, without requiring normalization. It cannot fix source data that is factually wrong. Adding a plain-language description of each data source at ingestion time helps the agent route queries correctly from the start.

What This Means for Your Data Engineering Roadmap

Start by assessing your current data sources and identifying which queries require hybrid data access. If you are building custom RAG pipelines for queries that cross structured and unstructured boundaries, you are solving an architectural problem with engineering effort that an agent can handle declaratively.

Plan for incremental agent deployment. Begin with five to ten data sources that represent your most common hybrid query patterns. Write plain-language descriptions for each. Verify results before adding more. As enterprise AI workloads mature, agents will be expected to reason across dozens of source types—including dashboards, code repositories, and external data feeds. The declarative approach makes that scaling tractable because adding a new source stays a configuration problem rather than an engineering one.

Your next step: list the queries your current RAG pipeline cannot answer. Those are the hybrid queries requiring multi-step agent architecture. That list is your implementation roadmap.