7 Key Steps to Build Agentic RAG Using CData

Agentic RAG enhances conventional retrieval-augmented generation by using specialized agents to plan queries, select and validate sources, refine retrievals, and escalate uncertain or high-risk decisions to human reviewers. The proposed implementation approach begins with measurable goals and confidence-based risk gates, then connects live enterprise data through CData Connect AI, a managed MCP platform that applies source-level permissions, semantic context, and audit logging. It recommends indexing trusted content with embeddings and metadata, assigning focused roles such as planner, router, validator, and summarizer, and using routing, reranking, and fallback procedures to handle simple requests, conflicting information, unavailable sources, and low-confidence results. The framework also emphasizes caching, limits on model calls and reasoning iterations, end-to-end tracing, resilience testing, and continuous evaluation of accuracy, latency, cost, and retrieval quality to support secure, reliable production deployments.

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