Knowledge Search Agent

Overview

The Knowledge Search Agent for Banking is a modular AI powered system that transforms how financial institutions access and interact with institutional knowledge. This multi agent solution streamlines knowledge retrieval across banking operations, delivering intelligent, conversational experiences for employees, compliance officers, and customer support teams. Built using Lyzr AI, the system decomposes key knowledge functions into specialized agents that handle policy interpretation, procedure lookup, regulatory reference, and cross-departmental queries. Unlike traditional static knowledge bases, this architecture provides dynamic, contextual, and auditable search experiences that integrate seamlessly with internal repositories like SharePoint, Confluence, and banking core systems.

Problem Statement

Banking environments suffer from fragmented institutional knowledge scattered across siloed systems including static intranet portals, compliance binders, outdated SharePoint folders, and inconsistent tribal knowledge. Employees waste valuable time searching for policies, asking repetitive questions, and relying on outdated documentation. Traditional knowledge management systems are static, keyword based, and disconnected from daily workflows, leading to high support volumes, slow onboarding, inconsistent policy enforcement, and operational inefficiencies. As compliance requirements and internal processes evolve rapidly, financial institutions struggle to maintain a reliable, accessible single source of truth. The industry needs a modular, AI native knowledge agent that can retrieve, summarize, contextualize, and present information conversationally, reducing friction while ensuring compliance and enabling employees to focus on value added tasks.

Agent Blueprint(Excalidraw diagram)

Agent Blueprint Flow Explanation

The Knowledge Search Agent workflow begins when the User Input Agent captures natural language queries through chat, form input, or voice, parsing intent and context for accurate routing. The Knowledge Retrieval Agent then employs semantic search and vector embeddings to identify the most relevant documents from the institutional knowledge base, including policy documents, SOPs, regulatory guidelines, and training materials. The Compliance Orchestration Engine routes retrieved knowledge to appropriate downstream agents for processing, formatting, and quality checks.

The Knowledge Summarization Agent converts retrieved information into concise executive summaries and contextual overviews optimized for quick consumption. The Insight Packaging & Delivery Agent formats and delivers final outputs through preferred channels such as Slack messages, PDFs, emails, or slide decks. Simultaneously, the Knowledge Gaps & Accuracy Monitoring Agent continuously assesses result quality, detecting outdated content or low-confidence answers to maintain trust and compliance.

The Auto-Improver Agent learns from user interactions to fine tune prompts and improve retrieval strategies over time. Finally, the Case Feedback & Model Trainer Agent incorporates user feedback to retrain embedding models, update source documents, and alert knowledge managers, ensuring continuous learning and content evolution. This orchestrated flow delivers contextual responses with source traceability while logging model feedback for ongoing improvement.

Benefits & Capabilities of the Agents

• Natural Language Understanding & Contextual Retrieval Interprets complex user queries in plain language and maps them to relevant policies and procedures without requiring keyword-based searches. Leverages advanced retrieval-augmented generation (RAG) to pull the most relevant information from distributed knowledge bases, ensuring responses are grounded in trusted content.
• Multi-Format Insight Delivery & Real Time Monitoring Delivers outputs in formats tailored to user preferences and workflows, including Slack messages, PDFs, emails, or slides. Continuously assesses the freshness and accuracy of retrieved information, flagging outdated content, hallucinations, or low-confidence responses for review.
• Feedback-Driven Learning Loop Captures user feedback at scale to automatically fine-tune prompts, update rankings, and retrain embeddings, keeping the system adaptive and user-centered while improving performance over time.
• Modular & Auditable Architecture each agent is modular, explainable, and independently upgradable, enabling audit trails, system transparency, and easy integration with enterprise systems including SharePoint, banking core systems, and policy repositories.

Tech Stack Used