Orchestration Agent Code Generation With Syntax Validation

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Solution Overview

Problem

Existing systems face challenges in efficiently generating accurate programming code due to the complexity of prompts required for Large Language Models (LLMs) and the inefficiencies in deploying orchestration agents, leading to higher resource usage and error rates, especially when chaining multiple logic modules for complex tasks.

Innovation Solution

The deployment of an orchestration agent that invokes specialized agents and modularized logic modules, including a programming code generation sub-LLM, to parse user input, retrieve relevant data, generate code, and validate syntax, reducing processing latency and resource usage through modularization and reusability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If a complex programming language is used to form logic for automated KPI monitoring and analysis, then the system can automatically obtain required data and generate graphical user interfaces, but the device complexity and difficulty of operation increase

Engineering Contradiction:

Improveautomated KPI monitoring and analysisVSAvoidcomplex programming language

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system segments the complex programming logic into modular components including data retrieval modules, analysis modules, and GUI generation modules. Each module handles specific aspects of KPI monitoring independently, reducing overall system complexity while maintaining automation capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

An intermediary layer is introduced between the complex programming language and the user interface. This layer translates complex backend logic into simplified user interactions, allowing automated monitoring without requiring users to understand the underlying complex programming language.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If multiple logic modules are chained for complex tasks to improve functionality, then the system can handle more comprehensive analysis, but processing latency and resource usage increase

Engineering Contradiction:

Improvecomprehensive analysis capabilityVSAvoidprocessing latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-compiling and caching analysis logic before actual KPI monitoring tasks. Common analysis patterns are pre-processed and stored, allowing the system to execute complex multi-module chains with reduced processing latency during runtime.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If multiple logic modules are chained for complex tasks to improve functionality, then the system can handle more comprehensive analysis, but resource usage increases

Engineering Contradiction:

Improvecomprehensive analysis capabilityVSAvoidresource usage

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system implements universal logic modules that can handle multiple types of analysis tasks. A single modular component can perform various KPI analysis functions depending on configuration, reducing the total number of specialized modules needed and thereby decreasing overall resource usage while maintaining comprehensive analysis capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Ease of operation

If LLMs are used to generate programming code from natural language, then ease of operation improves, but accuracy and error rates become challenging due to prompt complexity

Engineering Contradiction:

Improvenatural language interfaceVSAvoidcode generation accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system implements feedback mechanisms where generated programming code is automatically validated against the original natural language prompt and existing KPI monitoring requirements. Error detection and correction loops refine the generated code, improving accuracy while maintaining the ease of natural language operation.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The LLM-based code generation system performs self-service by automatically detecting and correcting errors in generated code without requiring external intervention. The system validates its own output against predefined criteria and iteratively refines the code to improve accuracy while maintaining user-friendly operation.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250370730A1Systems And Methods For Automated Generation Of Programming Code Through Deployment Of An Orchestration Agent

Publication Date: 2025.12.04 CISCO TECHNOLOGY INC

AI summary

Some implementations of the disclosure provide a computer-implemented method including operations of receiving a user question by an orchestration agent, where generating a response to a user question includes generation of programming code, executing, by a sub-large language model (LLM), an instruction to generate the programming code and performing, by the sub-LLM, a validation process including determining whether the programming code generated by the sub-LLM includes a syntax error. When the validation process indicates the programming code does not include the syntax error, the method includes operations of invoking a logic module configured to execute the programming code, wherein the logic module is provided the programming code generated by the sub-LLM and executes the programming code and generating, by the orchestration agent, a graphical user interface that displays the response to the user question that includes or is based on results of execution of the programming code.