Fixing Inaccurate Multi-Domain Answers with RAG and Multi-Agentic Workflows

The Problem

Teams handling complex enquiries—spanning legal, pricing, product, and operational domains—were limited by the accuracy and reliability of single-agent AI systems. Nuanced questions required synthesis across multiple knowledge areas, but single-model responses often produced brittle, incomplete, or contradictory guidance. This forced lengthy back-and-forth reviews with subject-matter experts, slowing output and increasing the risk of incorrect advice being circulated.

The Solution

One of our digital squads implemented a multi-agent orchestration framework built on top of retrieval-augmented generation. The system decomposed each query into domain-specific subtasks, routed them to specialist agents (Legal, Product, Pricing, Summariser), and used a coordinator agent to reconcile outputs, enforce contradiction checks, and return a verified composite answer.

The delivery included domain mapping, design of each agent with tailored prompt templates and retrieval parameters, and a governance layer featuring confidence scoring, source traceability, and human validation for high-risk or sensitive outputs. This created an auditable, reliable workflow for handling complex, multi-domain queries at speed.