Progress Agentic RAG

Progress Agentic RAG is an advanced retrieval-augmented generation platform designed to supercharge AI agents with enterprise-grade knowledge retrieval capabilities. Unlike traditional RAG systems that passively return documents, Progress Agentic RAG enables AI agents to take an active, autonomous role in the information-seeking process. Agents can iteratively query multiple knowledge sources, evaluate the relevance and quality of retrieved content, and synthesize information across disparate data repositories to deliver accurate, contextually rich responses. At its core, the platform bridges the gap between large language models and the vast, often siloed data landscapes within modern organizations. It supports connectors to a wide array of enterprise data sources, including databases, content management systems, file stores, and cloud services. This means AI agents powered by Progress Agentic RAG can access structured and unstructured data wherever it resides, eliminating the need for costly data migration or duplication. The architecture is built for scalability and security, ensuring that retrieval operations respect existing access controls and compliance frameworks. Agents operate within defined governance boundaries, making the platform suitable for highly regulated industries such as finance, healthcare, and government. Additionally, Progress Agentic RAG provides observability into the retrieval pipeline, giving developers and administrators visibility into how agents source, evaluate, and cite information—critical for building trust and debugging complex reasoning chains. By embedding intelligent retrieval directly into the agentic workflow, Progress Agentic RAG dramatically reduces hallucination risks, improves answer accuracy, and unlocks truly autonomous AI assistants capable of handling sophisticated, multi-step knowledge tasks across the enterprise.