These are historical times.
The Agentic Economy is here to stay.

Because grounding constrains what the system may claim — it was never meant to replace what the system must do. Strip the LLM out and see what's left: a search index that hands back twelve document chunks. That's enterprise search circa 2015, and its failure mode is exactly why RAG exists — it dumped the synthesis work on the human.

The LLM earns its place in four ways that retrieval can't touch. It understands the question: reformulates vague asks, resolves ambiguity, carries multi-turn context, decomposes “which suppliers renewed above the escalation cap in Q3?” into the sub-queries retrieval actually needs — in agentic RAG, the model runs the retrieval loop, deciding what to fetch next based on what it found. It reasons across sources: joins a clause from contract A with a price table from system B and a policy from doc C — no chunk contains the answer; the answer is a derivation over chunks. It transforms: 40 pages into one paragraph, legalese into plain language, a rule applied to a new case, a drafted email built from corpus facts. And in your world it acts — fills the system, triggers the workflow, escalates the incident.

There's also a quieter division of labor. The corpus supplies the facts of the instance — your counterparty's terms, your tolerance thresholds. The model supplies everything pretraining gave it: language competence, domain schemas (what a netting agreement structurally implies), reasoning patterns. Grounding is the contract that binds the two: general intelligence, instance-locked facts.

The analogy that settles it: a judge must ground every ruling in statute and precedent — nobody concludes the law library makes the judge redundant. The citation requirement disciplines the reasoning; it doesn't perform it.

The honest framing: retrieval finds, the model reasons and acts, grounding keeps it accountable. Nobody wants passages. They want conclusions they can trust and actions already taken.