GenAI on Your Terms

Being AI-agnostic is essential for RAG because no single LLM is best for every task. An AI-agnostic architecture lets enterprises choose the right model for each job, avoid vendor lock-in, control costs and easily adopt new models as they emerge. It also supports compliance needs, domain-specific models and multi-model workflows, facilitating the RAG pipeline to remain flexible, accurate and future-ready as AI evolves.

Agnostic AI benefits the business by giving teams the flexibility to use the best model for each job—optimizing for cost, accuracy and performance. It prevents vendor lock-in, allowing organizations to adapt as new models emerge or requirements change. This approach results in lower operational costs, faster innovation and AI solutions that stay compliant, resilient and aligned with evolving business goals.

You use agnostic AI by choosing a platform, like the Progress Agentic RAG solution, that lets you plug in, combine and switch between different LLMs without changing your architecture. The Progress Agentic RAG solution handles the retrieval, indexing and governance layers, so you can route each task to the model that delivers the best accuracy, cost efficiency or compliance for your needs. This allows teams to experiment, optimize and deploy AI solutions confidently, while keeping full flexibility as new models or business requirements emerge.

LLM-agnostic refers specifically to the ability to use any LLM within your AI system, while agnostic AI is a broader concept that means the system can work with any model, tool or technology—not just LLMs. In other words, LLM-agnostic is one component of agnostic AI. The Progress Agentic RAG solution is designed to be both: it lets you swap or combine different LLMs and also integrate various retrieval methods, data sources and agent workflows without being tied to a single vendor or technology stack.

Agnostic AI can introduce challenges, such as managing multiple model integrations and maintaining unified governance and security policies. The Progress Agentic RAG solution can standardize how models access data, evaluate outputs and coordinate tasks so the system remains reliable. Without this kind of infrastructure, organizations may struggle with complexity, interoperability and quality control as they work with diverse AI models and tools.

Yes, agnostic AI is highly scalable when supported by the right architecture. Because it isn’t tied to a single model or vendor, organizations can scale different parts of their AI workload independently—using smaller models for high-volume tasks, larger models for complex reasoning and switching providers as demand grows. The Progress Agentic RAG solution makes this even easier by handling retrieval, orchestration and governance at scale, facilitating consistent performance as you add more models, users or use cases.