A Rete rule engine decides loans, fraud and triage cases, then RAG over policy documents has an LLM explain the verdict, with an MCP server available.
A Rete rule engine decides loans, fraud and triage cases, then RAG over policy documents has an LLM explain the verdict, with an MCP server available.
The page describes a design in which a Rete rule engine makes the decision, so it is repeatable and auditable. Retrieval-augmented generation over your own documents then supplies the reasons behind the verdict. The source text I could read is only a short description, with no setup steps or details of agent actions.
The source states no results, numbers or limitations. It only describes the split in which rules decide what happens and retrieval explains why.
The fetched page held only a meta description, so check the site directly for setup, MCP details and costs.
Build a small rule-based decision engine for [use case, e.g. loan approvals] using the rules in [policy document or list of rules]. For each case I give it, output the decision, the exact rules that fired, and a plain-English explanation grounded in quotes from the policy document. Done means it correctly handles these [5] sample cases and I can review the audit trail for each.
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