Enterprise AI Agent Platform With Semantic Layer and Memory Governance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing enterprise-grade Large Language Model (LLM) implementations lack seamless integration with enterprise data systems, fail to provide adequate user access and knowledge retrieval controls, and struggle with flexibility, scalability, and integration across organizational functions, limiting the long-term value and evolution of AI systems.
Innovation Solution
A platform that integrates agentic AI creation, contextualized enterprise data interaction, and enterprise memory, utilizing a business function semantic layer to interpret domain-specific language, an enterprise memory engine for knowledge retention, and a governance framework for secure access, enabling cross-functional scalability and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized assistant architecture is used, then single-user interaction is simplified, but organizational scalability and cross-functional integration are limited
Solution Approach 1:
The system segments the centralized assistant architecture into distributed AI agents that can operate independently across different organizational functions. Each agent is specialized for specific tasks or domains, enabling both simplified user interaction and organizational scalability through modular deployment.
Solution Approach 2:
The AI agent platform provides universal capabilities that serve multiple functions across the organization. A single agent framework supports diverse use cases including data analysis, workflow automation, and decision support, enabling cross-functional integration while maintaining ease of operation through consistent interaction patterns.
2Reliability
If narrow data scope is used, then data security and governance are simplified, but integration capabilities with enterprise data systems are limited
Solution Approach 1:
The system introduces an intermediary layer between AI agents and enterprise data systems that handles security and governance concerns. This mediator manages data access policies, authentication, and authorization, allowing broad integration capabilities while maintaining simplified and reliable data governance through centralized control mechanisms.
3Adaptability or versatility
If limited governance framework is used, then deployment flexibility is improved, but user access control and knowledge retrieval controls are inadequate
Solution Approach 1:
The governance framework is designed to be dynamic and adaptable rather than static. Access control policies and knowledge retrieval permissions can be adjusted in real-time based on organizational needs, maintaining deployment flexibility while ensuring reliable access control through configurable security parameters that evolve with the organization.
4Device complexity
If static LLM assistant is used, then system simplicity is maintained, but collective intelligence capture and evolution are limited
Solution Approach 1:
The system incorporates feedback mechanisms where AI agents learn from organizational interactions and collectively evolve. Interaction data is captured and used to improve agent performance over time, enabling collective intelligence capture while maintaining relative system simplicity through automated learning processes rather than complex manual updates.
Data Source
AI summary
A computer-implemented method that includes receiving, via an artificial intelligence (AI) agent, a user prompt from a user. The method also can include translating, via the AI agent, the user prompt to an LLM prompt using a business function semantic layer to interpret business function-specific language in the user prompt. The method additionally can include obtaining a data query generated by an LLM based on the LLM prompt. The method further can include executing the data query on an enterprise data system to obtain datasets responsive to the data query. The method additionally can include generating a response to the user prompt using a qualitative analysis of the datasets. The method further can include providing the response to the user. Other embodiments are described.