Progress Agentic RAG
Generative AI infrastructure software
Generative AI software
Large language model operationalization (LLMOps) software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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$700 per month
Free Trial
Free version unavailable
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What is Progress Agentic RAG
Progress Agentic RAG is an LLMOps-oriented capability from Progress that helps teams build retrieval-augmented generation (RAG) applications with agent-style orchestration over enterprise content. It is used by developers and data/AI teams to connect large language models to internal data sources, manage retrieval and grounding workflows, and deploy AI-powered assistants or copilots. The product focuses on integrating data access, retrieval pipelines, and application logic so responses can be generated with supporting context from governed content.
RAG and agent orchestration focus
The product is designed around retrieval-augmented generation patterns, including multi-step agent workflows that can call tools and retrieve context before generating responses. This aligns with common enterprise use cases such as internal knowledge assistants, customer support augmentation, and document Q&A. Compared with general-purpose AI features embedded in collaboration tools, it targets build-and-operate scenarios for custom applications.
Enterprise data connectivity orientation
Progress’ portfolio and positioning typically emphasize connecting applications to heterogeneous enterprise data sources and services. For RAG implementations, this can reduce the amount of custom glue code needed to access content repositories and operational systems. It is relevant for teams that need to ground LLM outputs in internal data rather than rely on public web content.
Operationalization for production use
The product is positioned for deploying and running AI applications rather than only prototyping. In practice, this usually means providing mechanisms to manage prompts/workflows, control retrieval behavior, and integrate with existing application stacks. This can help teams move from experiments to repeatable deployments with clearer ownership and lifecycle management.
Limited public technical detail
Publicly available documentation and independently verifiable technical specifications for “Progress Agentic RAG” may be less extensive than for long-established open frameworks and dedicated search platforms. This can make it harder to evaluate supported vector stores, embedding/model options, and deployment architectures before engaging the vendor. Buyers may need vendor-led demos or trials to validate fit.
Ecosystem and portability tradeoffs
If key components (connectors, orchestration, or runtime services) are tightly coupled to Progress tooling, portability to other stacks may require rework. Teams that prioritize framework-level flexibility or want to swap retrieval and orchestration components independently may find constraints. This is a common consideration when choosing integrated LLMOps products versus modular libraries.
Governance and evaluation depth unclear
For enterprise LLMOps, buyers often require robust evaluation, monitoring, and governance features (e.g., offline test harnesses, quality metrics, auditability, and policy controls). Without clear, verifiable feature disclosures, it is difficult to confirm how deeply the product supports these requirements out of the box. Organizations with strict compliance needs may need supplemental tooling or custom processes.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Starter | $700 per month | 14-day free trial; Max 750 MB per file (text-based files only); 5 GB indexed data (or ~15K resources); Includes use of up to 10,000 tokens/month (additional tokens $0.008/token); RAG, Q&A; Community support; 1 Knowledge Box included; Unlimited named users. |
| Pro | $1,925 per month | Max 1.5 GB per file (all file types); 25 GB indexed data (or ~80K resources); Includes use of up to 10,000 tokens/month (additional tokens $0.008/token); RAG, Q&A, AI classification, generative search, AI Assistant, document summarization; Community support + Zendesk; 2 Knowledge Boxes included (up to 8); Customer Success onboarding included (up to 8 hours). |
| Enterprise | Custom pricing | Contact sales for a customized quote; Unlimited file size and indexed data; Full feature set including Prompt LAB / RAG LAB and custom AI models; Private Slack channel + Zendesk support; Cloud & Hybrid deployment options; Customer Success onboarding included (up to 24 hours). |
Additional pricing notes: Additional Knowledge Boxes beyond included allotments cost $750/month per extra Knowledge Box. Consumption price beyond included tokens is $0.008 per token. The vendor lists purchase availability via AWS Marketplace and direct purchase on Progress.com.
Seller details
Progress Software Corporation
Burlington, Massachusetts, USA
1981
Public
https://www.progress.com/
https://x.com/ProgressSW
https://www.linkedin.com/company/progress-software/
