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AI Agents: Built to Elevate Your RAG Pipelines

From data ingestion to validation, the AI agents embedded within the Progress® Agentic RAG solution elicit responses that are accurate, explainable and aligned with your organization’s real-world context.

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What Are AI Agents Built into Agentic Retrieval-Augmented Generation (RAG) Technology?

AI agents in the Progress Agentic RAG solution are intelligent, task-oriented components that automate the work users typically spend hours doing manually. These agents handle ingestion, retrieval, reasoning and multi-step workflows with speed and precision, enabling users to receive accurate, context-aware answers. By automating repetitive tasks like classification, content extraction, summarization and metadata enrichment, AI agents free up users to focus on higher-value work.

Key Capabilities

Data Augmentation Agents

  • Transforms messy enterprise content (PDFs, video, audio, tables, images, etc.) into structured, searchable and high-fidelity data without manual effort
  • Automatically extracts text, detects entities, converts media to transcripts and breaks content into meaningful semantic chunks
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REMi (RAG Evaluation Metrics)

  • Has a built-in evaluation agent, or an AI judge, that’s purpose-trained to assess the quality, groundedness and relevance of every answer your system generates
  • Provides organizations with a continuous quality assurance (QA) loop that reduces AI adoption risks, strengthens governance and provides the transparency required for regulated or mission-critical workflows
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Document and Paragraph Classification

Automatically classifies resources and paragraphs with business-specific labels, creating consistency across massive document sets and eliminating human error.

Graph Extraction Agent

Identifies entities (people, product, policies, etc.) and maps how they relate, transforming disconnected documents into a knowledge graph.

Q&A/Summarization Agent

Generates summaries, metadata and ready-to-use Q&A to transform static documents into interactive, digestible knowledge assets without requiring subject matter experts (SMEs).

Key Benefits:

  • Minimizes manual effort with autonomous data processing

  • Delivers trusted answers through agent-driven validation

  • Scales AI across the enterprise with end-to-end, low-lift workflows

Frequently Asked Questions

AI agents are software entities that can understand, reason and act to achieve a goal, often across multiple steps, without requiring a human to manually guide every action. Unlike a single Large Language Model (LLM) prompt that produces one answer and stops, an AI agent can break down tasks, gather information, make decisions and execute actions autonomously or semi-autonomously.

Data augmentation is the process of expanding, enriching or improving a dataset by creating additional, high-quality variations based on existing data. The goal is to give machine learning models more diverse, representative and robust training data, which leads to better accuracy, fewer errors and more consistent performance.

In AI, augmentation means enhancing or improving something—usually data, inputs or model performance—by adding new, helpful information or variations. The goal is to make the AI system smarter, more accurate, more resilient or more useful than it would be with the raw inputs alone.

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Index files and documents from internal and external sources to fuel your company use cases.

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