Query Indexed Resources with AI Agents - RAG Workflow from Web Data
The “Query” MCP prompt in Web Data Source (WDS) demonstrates a RAG (Retrieval-Augmented Generation) workflow applied directly to indexed web resources.
Instead of relying on simple keyword search or opaque retrieval pipelines, the AI agent executes a structured, explainable retrieval process that ensures results are both relevant and verifiable.
Each step forms a transparent reasoning chain, enabling trustworthy AI outputs for automation and decision-making.
The RAG workflow combines semantic understanding with controlled execution logic:
- identify the relevant indexed resource
- build a semantic query based on entities and intent
- retrieve candidate results using defined thresholds
- evaluate relevance of each result step-by-step
- expand the search using related concepts when needed
- perform deep retrieval on the most relevant sources
- generate structured, verifiable output
This transforms web resources into a reliable knowledge layer that AI agents can query with confidence.
Why It Matters
This approach delivers a RAG pipeline where retrieval is transparent, deterministic, and optimized for enterprise usage:
- AI agents query structured knowledge instead of raw HTML
- retrieval logic is explainable and auditable
- semantic expansion improves completeness of results
- deterministic workflows reduce ambiguity
- outputs are reliable for automated decision processes
Works Anywhere Your Data Lives
WDS can operate both on public Internet resources and inside isolated environments, supporting:
- Air-gapped compatible deployments
- Isolated environment deployment scenarios
- Secure data extraction with Zero-Trust architecture principles
- No internet dependency when required
- Private network crawling capabilities
- Data sovereignty compliance and enterprise-grade security
- Controlled access environments for sensitive workloads
Product Perspective
The Query prompt shows how WDS enables enterprise-ready RAG over web data, combining structured retrieval, semantic reasoning, and verifiable outputs to improve accuracy, reproducibility, and governance across AI workflows.
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