AI Agent Planning and Tool Integration for Precise Backend Interaction
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Solution Overview
Problem
Existing large language models (LLMs) face challenges in effectively connecting with backend systems, adapting to diverse customer requests, and ensuring seamless user experiences due to imprecise natural language interactions, bad data quality, and high computational requirements, leading to inefficiencies in software development and user interface complexities.
Innovation Solution
The introduction of next-generation AI agents that integrate LLMs with auxiliary systems, including an agent core, memory module, planner component, and tools, to process and reason about specific domains, leveraging Retrieval-Augmented Generation (RAG) for accurate responses, and employing a unified AI platform as a copilot for cloud services to enhance user experience monitoring and streamline software development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to connect with backend systems, then user interaction capability is improved, but interaction precision deteriorates due to imprecise natural language
Solution Approach 1:
The patent introduces an intermediary layer between natural language inputs and backend systems. This intermediary processes and refines the imprecise natural language queries into structured, precise requests that can be accurately interpreted by backend systems, thereby resolving the contradiction between adaptability to user interactions and precision in system operations.
2Measurement precision
If LLMs process complex requests, then response accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent segments complex requests into smaller, manageable sub-tasks that can be processed independently. By breaking down complex queries into simpler components, the system can maintain high response accuracy while reducing the computational burden on LLMs, as each sub-task requires fewer computational resources than the original complex request.
3Adaptability or versatility
If AI agents are built with multiple components, then functionality is improved, but system complexity increases
Solution Approach 1:
The patent designs AI agents with multi-functional components that can perform multiple tasks. Each component is built to be universally applicable across different scenarios and domains, allowing the system to achieve high functionality without proportionally increasing complexity, as the same components serve multiple purposes.
4Device complexity
If data quality is poor, then system simplicity is maintained, but response accuracy deteriorates
Solution Approach 1:
The patent implements preliminary data quality checks and cleaning processes before data is used by the LLM. By performing these actions in advance, the system maintains relative simplicity in data handling while ensuring that only high-quality data is processed, thereby preserving response accuracy without requiring complex real-time data validation mechanisms.
Data Source
AI summary
Systems and methods for next generation artificial intelligence agents include operating an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; receiving a request from a user; utilizing the planner to break the request down into a plurality of sub-parts that are each individually simpler than the request; and generating an answer to the request using the plurality of sub-parts with the memory and the one or more tools.