Build an Agentic Knowledge Layer Once
to Power Every AI Use Case

Traditional RAG uses a fixed pipeline that retrieves, then generates. Agentic RAG technology is dynamic: it analyzes your question, breaks it into sub-questions, selects the right sources and refines its retrieval on the fly. The result is more accurate, context-aware answers, especially for complex queries.

An agentic knowledge layer is the intelligent infrastructure that connects your data to your AI. It handles ingestion, indexing, retrieval and answer generation in one unified layer so your AI always draws from accurate, governed, enterprise knowledge rather than general training data.

The Progress Agentic RAG solution indexes your content (documents, audio, video and more) into a knowledge box. When a user asks a question, the platform performs semantic search to find the most relevant context, then passes it to a large language model (LLM) to generate a grounded answer. You control the retrieval strategy, the LLM and the pipeline configuration.

Retrieval agents are advanced components that go beyond basic RAG. They can query multiple knowledge sources, including knowledge boxes, SQL databases, internet search services and Model Context Protocol (MCP) servers simultaneously. They analyze questions, split them into sub-questions and dynamically decide which sources to use for each part.

Retrieval agents analyze the user's question and determine which source, or combination of sources, is most likely to contain the answer. This routing happens automatically at query time, without requiring any manual configuration per query.

Query rewriting transforms short or ambiguous queries into richer, more specific questions before retrieval begins. This improves semantic search accuracy and supports more relevant retrieved context, leading to better, more precise answers from the LLM.

Every answer is grounded in content retrieved from your knowledge box. The Progress Agentic RAG solution monitors groundedness, a metric that measures how closely the generated answer is tied to retrieved context. If groundedness decreases, the platform alerts you so you can adjust your retrieval strategy or update your knowledge base.

REMi is the built-in RAG evaluation model in the Progress Agentic RAG solution. It continuously monitors three key quality metrics: answer relevance (i.e., does the answer address the question?), context relevance (i.e., was the right content retrieved?), and groundedness (i.e., is the answer based on that content?) Results are tracked over time in the dashboard so you can catch quality regressions early.

A RAG pipeline that works well at launch can degrade over time as your data changes or usage patterns evolve. Continuous evaluation with tools like REMi facilitates AI that stays accurate, trustworthy and aligned with business requirements before users notice a problem.

Yes. Retrieval agents can query multiple knowledge boxes, SQL databases, internet search services and MCP servers within a single response. The agent determines which sources to use based on the question with no manual routing required.

Use agentic RAG technology when your questions are complex, span multiple sources or require reasoning across documents. It's also the right choice when you need enterprise-grade controls, such as security, compliance, custom LLMs and evaluation, alongside your retrieval pipeline.

General-purpose chatbots answer from their training data and predefined paths, which may be outdated, inaccurate for your domain or unable to cite sources. The Progress Agentic RAG solution answers exclusively from your indexed content, maintains conversation context and produces verifiable, grounded responses that are tied to your actual knowledge base.

Yes. The platform encrypts data at rest (AES-256) and in transit (TLS), supports role-based access control (RBAC) and includes built-in PII anonymization tools. It is compliant with GDPR, SOC 2 Type II and ISO 27001.

Yes. The Progress Agentic RAG solution is LLM-agnostic. You can switch between models, including OpenAI, Azure OpenAI, Anthropic Claude and Google Gemini, with a single configuration change. Your retrieval pipeline, knowledge base and evaluation setup remain intact.

The Progress Agentic RAG solution supports a wide range of enterprise use cases, including enterprise search, customer support automation, document Q&A, e-commerce product discovery, multi-modal search (text and images) and any application that requires grounded, accurate answers over private data.

You can sign up, create a knowledge box and ingest your first documents in minutes at rag.progress.cloud. A no-code dashboard lets you upload files, test your search engine and embed a widget into your application, all without the need to write code. API and SDK options are available for deeper integrations.

The Progress Agentic RAG solution supports 30+ content types, including plain text, HTML, Markdown, JSON, PDFs, Word documents, images (JPEG, PNG, TIFF), video (MP4, AVI, MPEG) and audio (MP3, WAV) files. Any resource you upload becomes searchable and available for AI-generated answers.

It extends it. Traditional enterprise search returns links and snippets. The Progress Agentic RAG solution delivers direct, synthesized answers grounded in your content with natural language queries, multi-modal support and the ability to reason across multiple sources. Many teams use it alongside, or on top of, existing search infrastructure.

The most common challenges are: (1) Coverage Gaps: if the answer requires connecting information across documents that aren't indexed together, retrieval quality suffers; (2) Pipeline Tuning: choosing the right embedding model, retrieval strategy and LLM takes iteration; and (3) Quality Drift: pipelines need ongoing monitoring (via tools like REMi) as data and usage evolve. The Progress Agentic RAG solution provides the tools to address all three.