Technical Tuesday: How DeepRAG helps AI agents evolve from retrieval to comprehension

Achieving transformative AI value isn’t about finding the right information. Enterprises already have AI agents that can find information—but retrieval doesn't mean understanding. Simple retrieval-augmented generation (RAG) is good at answering factual questions by retrieving relevant data and summarizing it.

Yet, building and deploying AI agents for your most valuable processes often requires complex context engineering based on information scattered across hundreds of pages of dense, siloed documents. This is where traditional RAG falls short: it can fetch relevant snippets, but it can't truly understand the problem you’re trying to solve. RAG doesn’t reconcile contradictory clauses, apply domain rules, or trace how one exception changes the outcome elsewhere.

Transformative value appears only when an AI agent can assemble context, test hypotheses against evidence, and justify its recommendation with a transparent chain of reasoning. In other words, the hard part isn’t finding context—it’s understanding it.

Why AI agents must evolve from information retrieval

In the real world, your most critical information isn't found in a single, clean database. It’s fragmented across multiple data silos: a patient's history is in 50 different clinical notes; a contract's risk is hidden in three amendments and a separate schedule; and a product's manufacturing process is detailed in batch records, quality reports, and regulatory filings.

Simple retrieval-based agents fail here. They might find a relevant snippet, but they can't see the full picture. They can't connect the dots, identify conflicts, or synthesize a novel insight from 12 different sources. When they try, they smooth out or ignore conflicting information, miss effective dates and definitions, and produce summaries that can’t be audited.

Enterprises are brushing up against the limits of what traditional RAG can offer. As businesses attempt more complex and sophisticated agentic use cases, they need complex reasoning across documents, handling of uncertainty and edge cases, and citations for every output, so all decisions are compliant and defensible. In short, RAG needs to evolve.

Introducing DeepRAG: unlocking true comprehension

To be truly useful in the enterprise, AI agents must be able to:

• Cut through silos: actively synthesize unstructured data from across the business

• Ensure compliance: provide a transparent, auditable trail for every piece of information sourced and every decision made

• Analyze, not just summarize: answer complex questions that require cross-document reasoning

This is where DeepRAG (deep research-augmented generation) comes in. Developed by UiPath, DeepRAG is an advanced AI system that gives AI agents the power of true comprehension.

More than traditional RAG, DeepRAG is a production-ready system that enables agents to perform deep synthesis across multiple documents. It doesn’t simply take the shortest path to find the information it thinks the user wants. The system uses agentic reasoning to intelligently plan, research, question, and synthesize information from document sets as large as 1,000 pages in a single query. This enables AI agents using DeepRAG to answer complex queries with comprehensive, citation-backed answers.

How does DeepRAG work?

DeepRAG uses a sophisticated, multi-step agentic workflow to mimic the process of a human researcher. Instead of a simple "retrieve-and-answer" process, DeepRAG operates in three distinct phases:

Phase 1: initial planning

When DeepRAG receives a complex query like ‘What are this patient's cardiac risk factors?’ it doesn't just start searching. First, it analyzes the user's intent and breaks the complex question down into a logical plan with concrete sub-questions.

For the patient question, this breakdown would look like:

• Find all cardiac diagnoses

• Identify relevant medications, family history, and lifestyle factors

• Synthesize all findings into an overall risk assessment

This planning phase ensures the agent knows what it's looking for before it begins.

Phase 2: iterative research loop

This is the core of DeepRAG's power. The agent executes its plan in an iterative research loop. This is a cycle where the agent:

• Plans its next research action

• Selects the right data source or index

• Queries and retrieves evidence

• Extracts the key information

• Consolidates it with its previous findings

• Revises its plan based on what it just learned and repeats the loop

The key innovation is that DeepRAG builds a comprehensive body of knowledge step-by-step, just as a human expert would.

Phase 3: final synthesis

Once the research loop is complete and all the evidence has been gathered, DeepRAG moves to final synthesis. It feeds all the consolidated evidence to a final generation step, which produces a single, cohesive, and comprehensive answer to the user's original, complex question. Every finding in this final report is backed by detailed citations, right down to the document name and page number.

DeepRAG: Value-adding use cases

DeepRAG is already in production, with over 17 customer implementations with documented ROI including a 5-10x reduction in document review time. Here’s a few examples of how DeepRAG solves complex document synthesis problems across major industries:

1. Healthcare: medical record summarization (MRS)

This is DeepRAG's flagship use case, achieving a 5-10x reduction in review time proven by UiPath healthcare provider and payer customers.

A clinician needs to review a patient's history, but the information is buried in 20-400 pages of clinical notes, lab results, imaging reports, and medication histories. The DeepRAG agent can read the entire file and generate a structured, multi-section summary that includes chief complaints, diagnoses, medical history, medications, and allergies, with citations for every single finding.

2. Financial services: contract and covenant analysis

Leading banks already use DeepRAG for high-stakes financial analysis. For example, analyzing a commercial credit agreement requires reading the main contract, all amendments, and supporting schedules to understand risk. DeepRAG streamlines this by connecting and understanding all documents to produce a summary of key terms (principal, interest rate), financial covenants (with their specific thresholds), collateral, and default provisions. It can also be prompted to flag any terms that deviate from standard bank policy.

3. Pharmaceuticals: tech transfer documentation

Transferring a manufacturing process from an R&D site to a production plant requires synthesizing dozens of documents: batch records, manufacturing procedures, quality control data, equipment specifications, and regulatory submissions. Luckily, an AI agent using DeepRAG can create a ‘tech transfer summary’ that identifies critical process parameters, in-process controls, and validation status. Most importantly, it can identify gaps and risks between the sending and receiving sites, recommending mitigation strategies before the transfer begins.

Get started with DeepRAG

DeepRAG harnesses the latest Gemini models to deliver its breakthrough comprehension capabilities. Through rigorous testing on real enterprise document sets, we found that our Gemini-powered implementation delivers a better balance of quality, cost, and latency compared to any other hyperscaler foundation models. As we continue to develop DeepRAG, we'll ensure our customers continue to have access to the most powerful AI capabilities available.

Today, you can enable DeepRAG for your AI agents directly within Agent Builder in UiPath Studio. Visit DeepRAG release notes and DeepRAG How-To Guide to learn more, or try it out in our sandbox environment with pre-loaded examples at playground.uipath.com.

When you're ready to test it on your own complex contracts, medical records, or technical manuals, sign up for a UiPath Automation Cloud trial. Once you're in, you can request DeepRAG access to unlock this capability and see what your agents can really do.