Turn governed business evidence into AI-assisted action with RAG

Standard RAG follows a designed sequence: accept a request, run the selected search, assemble context, and call the model. Microsoft's current RAG architecture guidance recommends considering agentic RAG when the task truly needs multistep reasoning, dynamic source selection, query decomposition, or repeated retrieval. MetaCTO starts with the fixed path when one governed query can answer the case because it is easier to evaluate, authorize, and recover. We add agent-directed retrieval only when representative cases prove that the extra planning improves the operational result, then constrain the sources, tool calls, stopping rules, and approval boundary.

Choose by the job. Search is usually clearer when a person only needs to locate an exact record. Direct prompting can be simpler when a small, permitted source set fits safely in the model context. Fine-tuning can change task behavior or output style, but it is an awkward mechanism for facts that must be updated, removed, or cited. The original RAG research paired a generative model with explicit non-parametric memory partly to improve provenance and make knowledge easier to update. MetaCTO therefore selects RAG when a workflow needs synthesized, sourceable context at request time, not merely because embeddings are available.

Retrieval must begin with an authenticated actor, tenant, business purpose, and enforceable source scope. Microsoft's secure multitenant RAG guidance makes the core requirement explicit: a tenant or user must only incorporate grounding data they are authorized to access. MetaCTO translates identity claims into filters or isolated indexes before candidate passages reach the model, tests both allowed and denied cases, and carries source and revision identifiers into the answer. Metadata filtering helps enforce the decision, but it does not replace the identity system, authorization policy, or an auditable denial path.

Test retrieval and generation separately before judging the workflow end to end. Current Azure Architecture Center guidance identifies retrieval measures such as precision at K, recall at K, and mean reciprocal rank, then recommends evaluating generated responses for groundedness, completeness, utilization, relevancy, and correctness. Because model responses are nondeterministic, it advises using target ranges rather than a single expected score. MetaCTO adds permission-denial cases, required-source and no-answer cases, citation checks, reviewer corrections, action accuracy, and the business queue's final disposition so a fluent answer cannot hide a retrieval or operating failure.

Treat every retrieved passage as evidence, never as a trusted instruction. Current RAG security guidance warns that malicious instructions can arrive through retrieved documents, while source changes and deletions can make an apparently relevant passage invalid. MetaCTO records source ownership, revision, effective date, access metadata, ingestion receipt, and deletion state; isolates retrieved text from system policy and tool permissions; and asks the model to abstain when mandatory evidence is missing or contradictory. Consequential recommendations go to a reviewer with the exact passages, and only the surrounding workflow can authorize and reconcile a write-back.