GenAI Agents for Knowledge Interface
Introduction:
There are specialized AI models – each designed to excel at a particular task. These models can write, analyze or even classify documents. These models are like skilled specialists, each performing their particular role with expertise. The ability to coordinate these capabilities, to understand context, and to make decisions about what to do next is missing.
AI Agents:
The above missing ability created significant challenges. Because of this inability users find difficulty to manage complex workflows, lose valuable context between steps, and lack the adaptability needed for real-world tasks.
This is where AI agents revolutionize the landscape. An agent acts more like a skilled coordinator, using multiple capabilities while maintaining a holistic understanding of the task. Like a human expert it can make more informed decisions about what to do next based on what it learns along the way.
Knowledge Interface for Sales team using Agents:
The Sales team required a unified knowledge interface that could ingest and intelligently surface insights from both structured data (such as CSV files, CRM databases, or sales spreadsheets) and unstructured content (like PDFs, DOCX reports, product briefs, and meeting notes). To address this, we designed a dual-pipeline knowledge system composed of two primary components: Structured Knowledge and Unstructured Knowledge.
For structured data, we leveraged Text-to-SQL capabilities, allowing users to pose natural language questions that are translated into executable SQL queries. This provides precise, tabular answers directly from relational datasets. In contrast, for unstructured knowledge, we implemented Retrieval-Augmented Generation (RAG) techniques. Here, documents are parsed, chunked, embedded, and stored in a vector database, enabling the system to retrieve semantically relevant content and generate contextual answers using a language model.
However, real-world queries often span both data types — for example, “Which deals closed last quarter that involved products mentioned in the Q2 market analysis?” This is where a standalone solution for structured or unstructured data would fall short. To bridge this gap, we introduced an AI Agent — a smart orchestration layer that serves as a query router.
The AI Agent classifies each incoming user question based on its intent and data requirements. It then dynamically selects the appropriate retrieval strategy — whether it’s querying a SQL database, performing a semantic search, or invoking a hybrid workflow involving both. This intelligent delegation not only streamlines the user experience but also ensures comprehensive, context-rich answers without sacrificing data accuracy or relevance.
Conclusion
By deploying this agent-based architecture, we’ve successfully broken down the traditional barriers between structured and unstructured knowledge within the organization. Sales teams no longer need to navigate disparate systems or know where specific data resides — whether it’s buried in a CRM table, a quarterly PDF report, or a product brief in a shared folder. Instead, they can interact with a single, intelligent interface that understands their intent and dynamically determines the optimal retrieval strategy.
This AI agent doesn’t just answer queries — it orchestrates the reasoning required to access and synthesize information from multiple sources in real time. Whether it’s generating SQL to extract numbers from a database or semantically matching a customer question to a paragraph in a proposal, the agent ensures that the most accurate and context-aware information is surfaced.
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