Solving the Multi-Hop Failure Mode

Standard RAG systems often fail on complex, multi-hop queries that require synthesizing information across disparate databases. When a query requires multiple steps—such as identifying a server ID in one database and then fetching its specifications from another—vanilla RAG often returns partial or incorrect results because it lacks the ability to plan and iterate. Google’s new Agentic RAG, integrated into the Gemini Enterprise Agent Platform, addresses this by treating search as an iterative research process rather than a single-step retrieval task.

The Multi-Agent Architecture

The framework replaces the standard single-pass retrieval with a specialized team of agents:

• Orchestrator: Manages the overall request and delegates tasks.
• Planner Agent: Maps out the necessary information pathways across various data sources.
• Query Rewriter: Breaks down complex, vague user requests into precise, searchable queries.
• Search Fanout Agent: Executes queries across multiple retrieval sources simultaneously.
• Sufficient Context Agent: The critical innovation that inspects retrieved snippets against the prompt, identifies missing information, and logs specific gaps for further searching.
• Synthesis Agent: Aggregates the verified, complete context into a final, accurate response.

Performance and Benchmarks

Tested on the FramesQA dataset (824 queries across 2,676 documents), the framework demonstrated significant improvements in factuality. In a cross-corpus setting—where the system must route queries to the correct database among four options—it achieved 90.1% accuracy, nearly matching its performance in single-corpus environments. Notably, this iterative process adds minimal overhead, with latency remaining within 3% of standard retrieval methods. This approach is particularly effective for enterprise use cases like healthcare record synthesis, engineering asset tracking, and cross-departmental financial reporting.