Microsoft Research's AgenticRAG work explores multi-step retrieval for questions that require planning across enterprise knowledge. BenchmarkQED focuses on repeatable evaluation of retrieval-augmented systems, while Microsoft Foundry documents separate evaluators for retrieval and response quality. A further Microsoft Research paper highlights participant-aware access control. Together, these sources identify design and evaluation directions; they do not prove that one RAG pattern is universally best.
What can be measured
Separate retrieval quality from answer quality. Test whether the system found the necessary evidence, whether the response stayed faithful to it, and whether the user was allowed to see it. Include outdated, conflicting, missing, and access-restricted documents in the evaluation set.
Where simple retrieval is insufficient
Questions spanning several sources may benefit from planned or iterative retrieval, but extra steps add latency, cost, and new failure modes. Use them only when representative tests show a material improvement.
Practical conclusion
Treat RAG as an evidence pipeline, not a guarantee of truth. Version the corpus and retrieval settings, preserve citations, enforce access before generation, and benchmark every material change against the same task set.
Sources & further reading
Follow the original evidence. Sources may include the organisation making the announcement; claims and independent findings are distinguished in the analysis.
01AgenticRAG, Microsoft Researchwww.microsoft.com02BenchmarkQED, Microsoft Researchwww.microsoft.com03RAG evaluators, Microsoft Foundrylearn.microsoft.com04Participant-aware access control, Microsoft Researchwww.microsoft.comRetrieval-Augmented Generation in 2026: What Works, What Doesn't, and What's Next
Recent research and evaluation tooling emphasise retrieval quality, benchmarking, multi-step search, and access control; no architecture guarantees grounded answers.
Last factual review: 24 August 2026