Agentic RAG - GoInsight.AI

Overview

Manually researching complex questions wastes time and leaves gaps in insight. Agentic RAG solves this by iteratively querying internal knowledge bases and, if needed, the web—automatically assessing coverage and generating new queries until the information is complete. It then compiles a clean, structured report ready for immediate use.

Generated by AI

The Impact

Automate Iteration. Eliminate manual back-and-forth by running multi-round searches that fill missing info.
Boost Accuracy. Merge and deduplicate diverse data sources to avoid redundant or outdated info.
Expand Coverage. Combine internal and web data dynamically to cover all angles.
Deliver Ready Reports. Generate structured summaries that directly answer your query.

Who This Is For

Product Managers conducting rapid market research with internal and public data.
R&D Teams completing technical feasibility studies by combining proprietary and external resources.
Competitive Intelligence Analysts continuously tracking and summarizing competitor info.
Data Researchers seeking comprehensive, verified content synthesis.

How It Works

Initialize Parameters
Set up core variables like ActiveQuery, empty results list, and rationale storage for iteration.

Iterative Knowledge Base Search
Search internal data for ActiveQuery, merge new results while deduplicating by BlockId, and check if new info was found.

Assess and Generate Next Query
Evaluate if current info suffices; if not, formulate a new, optimized query to fill gaps or pivot search angle.

Optional Web Search & Crawl
When enabled, generate high-quality Google queries, execute searches, extract non-PDF links, and batch crawl with AI summarization.

Compose Final Report
Integrate all data sources into a structured, concise report that directly answers the initial query.

What You'll Need

Before using this template, make sure you have:

Access to internal knowledge bases or uploaded searchable documents for retrieval.
Optionally, permission and API access for web search and web crawling tools if external data supplementation is desired.
Clear initial query phrased as a concise question or statement to trigger relevant searches.
Environment supporting iterative workflow execution and handling of JSON data merging and deduplication.

How to Use

Step 1. Prepare Your Query

Enter a clear and focused question or statement as your initial query to start relevant retrieval.

Step 2. Configure Search Scope

Decide whether to enable web search to supplement internal knowledge bases or rely solely on internal data.

Step 3. Run Iterative Retrieval

Let the workflow iteratively search, evaluate, and refine queries to fill knowledge gaps automatically.

Step 4. Review Crawled Content

If web search is enabled, the workflow will crawl and summarize relevant web pages to enrich results.

Step 5. Verify Final Report

Inspect the generated structured report to ensure it fully addresses your original query and meets quality expectations.

FAQs

How does Agentic RAG decide when to stop searching?

It assesses if accumulated information sufficiently answers the query. If new relevant data is lacking or no further queries improve coverage, the iteration ends.

Can I use this workflow without enabling web search?

Yes, you can rely solely on your internal knowledge base by setting UseWebSearch to false, focusing the workflow on internal data retrieval and synthesis.

How does the workflow handle duplicate or overlapping information?

It merges results and removes duplicates based on unique BlockId identifiers, ensuring the final data set is clean and non-redundant.

What if the initial query is too narrow or yields no results?

The system’s logical analyst node generates new, optimized queries that pivot or broaden the search scope to fill gaps effectively.

Updated on: Apr 2, 2026

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