Benefits
This is the chat interface that practitioners have been assembling piecemeal from separate tools. One app. Every FileSet. Every dataset. Built-in RAG. Persistent history. No per-source configuration.
- Universal data access: A single chat interface connects to every FileSet and every dataset in the instance, eliminating the need to know which data source contains the information you need before you can ask the question
- Built-in RAG without setup: FileSets are automatically embedded for retrieval-augmented generation, meaning document-based queries work immediately without any preprocessing pipeline or vector database configuration
- Persistent conversation history: Chat history is maintained across sessions, allowing users to reference previous queries, build on earlier analysis, and maintain context in ongoing research threads
- Elimination of per-source configuration: Traditional RAG implementations require configuring each data source individually. This agent connects to everything by default, removing the setup overhead that prevents most organizations from achieving universal data access
- Natural language querying: Users ask questions in plain language and receive answers synthesized from across the entire data estate, whether the answer lives in a PDF in a FileSet or a column in a dataset
- Instant time-to-value: Because the agent connects to all existing data sources automatically, it provides useful answers from the moment it is deployed without requiring data migration, indexing, or configuration
Problem Addressed
Here is what the data access experience looks like in most organizations: you know the information exists somewhere, but you do not know which dataset or document contains it. So you search through file directories, scan dataset names, open a few likely candidates, and eventually find what you need twenty minutes later. Now multiply that by every question, every day, every person in the organization.
RAG-based chat interfaces solve this problem beautifully in theory. In practice, every implementation requires configuring each data source individually: connecting the source, building the embedding pipeline, indexing the content, and maintaining the index as content changes. For organizations with dozens or hundreds of FileSets and datasets, the setup overhead means that most data sources never get connected, and the chat interface only has access to a fraction of available knowledge. The gap between the RAG promise of "ask anything" and the RAG reality of "ask about the three sources we had time to configure" is where this agent lives.
What the Agent Does
The agent operates as a universal chat interface with automatic connection to every data source in the instance:
- Automatic FileSet discovery and embedding: Scans all available FileSets in the instance and builds embeddings for each document, enabling retrieval-augmented generation across the complete document corpus without manual configuration
- Universal dataset connection: Connects to every dataset in the instance, enabling structured data queries across the full data estate without specifying which dataset to search
- Conversational query interface: Provides a natural language chat interface where users ask questions and receive synthesized answers that draw from both unstructured documents in FileSets and structured data in datasets
- RAG-powered document retrieval: When a query relates to document content, the agent uses embedding similarity to identify the most relevant documents and passages, then synthesizes an answer grounded in the retrieved content
- Structured data analysis: When a query relates to structured data, the agent identifies the relevant dataset, constructs the appropriate query, and returns formatted results with context and interpretation
- Persistent chat history: Maintains full conversation history per user, enabling contextual follow-up questions, reference to previous answers, and long-running research threads that build on accumulated context
Standout Features
- Zero-configuration data coverage: The agent automatically connects to and indexes every available data source in the instance, achieving complete coverage without requiring administrators to configure each source individually
- Hybrid structured and unstructured querying: A single question can trigger both document retrieval from FileSets and data queries from datasets, synthesizing answers that combine insights from both modalities
- Automatic embedding maintenance: As new documents are added to FileSets or existing documents are updated, embeddings are automatically refreshed to keep the RAG index current without manual reindexing
- Source attribution: Every answer includes references to the specific documents, passages, or datasets that contributed to the response, enabling users to verify answers against source material
- Conversation threading: Users can maintain multiple active conversation threads for different research topics, with each thread preserving its own context and history independently
Who This Agent Is For
If you have been building separate RAG pipelines for different data sources, this agent is the consolidation you have been planning but never had time to build.
- All platform users who need to find information across the organization's complete data estate without knowing which specific source contains the answer
- Analysts who routinely search across multiple datasets and document repositories to answer business questions
- Knowledge workers who need quick access to information stored in uploaded documents, reports, and reference materials across FileSets
- Platform administrators who want to provide a single, powerful data access interface to their user base without configuring individual data source connections
Ideal for: Any organization with a large, distributed data estate across both structured datasets and document FileSets where the time spent searching for information represents a measurable productivity cost.
