Abstract
This article introduces an Agentic RAG (Retrieval-Augmented Generation) system designed for complex tasks. Its core capability lies in completing research tasks that would traditionally take a human expert hours in just 2–4 minutes, achieved through multi-round iterative retrieval, dynamic research planning, and structured report generation. The system achieves an accuracy of 21.1% on the comprehensive benchmark Humanity’s Last Exam and 93.9% on the factual question-answering benchmark SimpleQA. This article breaks down its technical workflow, operational boundaries, and deployment challenges, while also providing a reference path for open-source implementation.
Problem Background
Standard RAG systems typically use a "single retrieval + single generation" model. This is suitable for factual question-answering but falls short when handling complex queries that require multi-hop reasoning, cross-source validation, or inductive synthesis.
For example, a query like "analyze the commercialization prospects of an emerging technology" requires not only gathering information on its technical principles, patent landscape, and market dynamics but also conducting a horizontal comparison of competitors, assessing policy risks, and integrating everything into actionable conclusions.
To address these high-level tasks, a new Agentic RAG architecture has been proposed. Instead of passively responding, the system actively plans its research path, simulates the behavior of a human expert, and ultimately outputs a structured report.
Methodology Overview
The system's workflow is divided into three stages:
1. Autonomous Research and Reasoning
The system has search and code execution capabilities, enabling it to:
- Generate multiple sub-questions in the initial phase;
- Iteratively execute a Search → Read Documents → Assess Information Gaps → Adjust Subsequent Strategy loop;
- Call a code interpreter when necessary (e.g., to parse tables or calculate metrics) to enhance fact-checking.
2. Report Writing
After information gathering is complete, the system deduplicates, categorizes, and synthesizes hundreds of sources to generate a logically coherent, citable structured report, not just a simple summary.
3. Result Export
Supports exporting to PDF or document formats for easy archiving and collaboration.
Efficiency: The entire process takes about 3 minutes on average, a significant improvement over manual research efficiency.
Key Technical Details
1. Dynamic Research Planner
- Uses a large language model as a "research agent" to dynamically generate the next search keywords based on its current state of knowledge;
- If it detects conflicting information or insufficient coverage, it proactively expands its data sources or delves deeper into specific sub-domains;
- Example: If an initial query about "a company's technical advantages" doesn't cover competitor comparisons, it automatically generates sub-queries like "vs major competitors."
2. Multi-Source Hybrid Retrieval
- Calls multiple modern search engines in parallel (such as services that support the Model Context Protocol (MCP));
- Performs multi-source cross-validation for key facts (e.g., financial data, technical specifications);
- Incorporates a confidence mechanism, where low-confidence content is down-weighted or excluded.
3. Structured Output Generation
- The report is organized into logical modules (Background, Methodology, Core Findings, Conclusion);
- Each claim is accompanied by a source link for traceability;
- Supports rich formats like tables and comparison lists to enhance readability and utility.
The system demonstrates outstanding performance on two authoritative benchmarks:
| Benchmark | Description | Accuracy |
|---|
| Humanity’s Last Exam | A comprehensive test covering 100+ subjects and 3,000+ questions | 21.1% |
| SimpleQA | Tests factual question-answering capabilities | 93.9% |
- On Humanity’s Last Exam, its performance significantly surpasses mainstream models like o1, DeepSeek-R1, and Gemini Thinking;
- Over 90% of tasks can be completed within 3 minutes, balancing depth with efficiency.
Limitations and Engineering Challenges
Despite its impressive results, this architecture faces the following challenges in practical deployment:
- High Computational Cost: A single task involves dozens of retrieval API calls and multiple LLM inferences, with costs roughly proportional to task complexity;
- Latency Constraints: The 2–4 minute response time is unsuitable for real-time conversations or low-latency scenarios;
- Dependence on External Data Quality: If the retrieval sources contain noise, bias, or outdated information, the reasoning chain can become contaminated;
- Lack of User Intervention Mechanism: The current process is fully automated, with no way to correct the research direction or priorities mid-stream.
Future improvement directions include:
- Introducing a user feedback loop;
- Supporting partial result previews;
- Optimizing caching and reuse strategies for intermediate results.
Open-Source Implementation Recommendations
If you want to quickly build a deep research system with the capabilities described above, we recommend using the open-source product Deep Wide Research Agent by puppyone:
- Built on the Model Context Protocol (MCP), it supports plug-and-play integration of data sources and tools;
- Provides an intuitive Depth × Wide Control Plane, allowing users to flexibly adjust research complexity and coverage with two parameters;
- Includes built-in logic for estimating resource consumption to help developers predict costs;
- Supports fully private deployment, ensuring that sensitive corporate data remains within your domain;
- Compatible with various model backends like OpenAI, Claude, DeepSeek, and local LLMs, meeting both compliance and performance requirements.
Use Cases: Financial analysis, market research, technology assessment, health consulting, travel planning, etc. It can serve as an "automated research assistant" within an organization.
👉 Try it out: https://www.deepwideresearch.com
FAQ
Q1: What is the fundamental difference between this system and a standard question-answering model?
Standard models rely on a single context to generate an answer. In contrast, this system has autonomous planning capabilities, allowing it to proactively identify information gaps, perform iterative retrieval, cross-validate facts, and output a structured report.
Q2: Does it have to rely on internet search?
Yes, the current architecture relies on the live web to obtain the latest information. To process private knowledge (like corporate documents), you would need to integrate an internal knowledge base and ensure the retrieval module supports hybrid sources (public web + private). The Deep Wide Research Agent supports connecting to local knowledge bases.
Q3: Can the 3-minute latency be reduced?
It can be optimized by reducing the breadth (i.e., the number of data sources), enabling caching, and parallelizing retrieval. However, the deep reasoning process itself has a computational lower bound. For latency-sensitive scenarios, a combined strategy of a "fast mode" plus manual review is recommended.
