Collaborative AI Agent Workflows for Industry-Specific Coordination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI systems lack the ability to coordinate multiple agents within a specific industry context, leading to inefficiencies and long lead times in processes like new product introduction, as they are typically generic and do not incorporate domain-specific data structures or decision criteria.
Innovation Solution
A system and method for generating industry-specific solutions using collaborative AI agents that analyze input data to identify a goal context, select appropriate workflows, retrieve relevant data, and execute tasks using a plurality of AI agents with compatibility rules, generating candidate solutions through a Generative Artificial Intelligence model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic multi-agent frameworks are used, then system complexity is reduced, but industry-specific coordination capability deteriorates
Solution Approach 1:
The system segments the multi-agent framework into generic core components and industry-specific adapter layers. The generic framework provides base functionality, while industry-specific adapters (e.g., RetailAdapter, HealthcareAdapter) inject domain knowledge through configurable parameters and industry-specific data structures, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system uses parameter-based configuration to switch between different industry contexts. By changing parameters such as industry type, workflow templates, and domain-specific rules, the same underlying framework can adapt to different industries without structural modifications, maintaining low complexity while achieving high adaptability.
2Ease of operation
If manual coordination among teams is used, then flexibility in decision-making is maintained, but process efficiency deteriorates
Solution Approach 1:
The AI agents autonomously perform coordination tasks among teams without requiring manual intervention. Agents can independently schedule meetings, allocate resources, and resolve conflicts based on predefined rules and real-time data, thereby maintaining operational flexibility while dramatically improving process efficiency through automation.
3Device complexity
If existing AI systems operate independently, then individual agent simplicity is maintained, but collaborative problem-solving capability deteriorates
Solution Approach 1:
The system merges multiple independent AI agents into a coordinated collaborative framework. Each agent maintains its simplicity and specialized function, but through the orchestration layer, they combine their capabilities to solve complex industry-specific problems, achieving high reliability in collaborative problem-solving without increasing individual agent complexity.
Data Source
AI summary
Systems and methods for generating industry-specific solutions using collaborative Artificial Intelligence (AI) agents are disclosed. In an aspect, input data corresponding to an industry-specific problem is received. A goal context for the industry-specific problem is then identified. Further, an industry-specific process workflow corresponding to the industry-specific problem is selected based on the goal context. Furthermore, an agentic context, historical intelligence data, group dynamics data for agent compatibility, appropriate agent character data, and historical user feedback data corresponding to the industry-specific workflow are retrieved. Moreover, AI agents and agent compatibility rules to execute user goals are selected and the rules are assigned to each AI agent. An agentic process workflow for the industry-specific problem is then generated. A candidate solution is then generated by executing the generated agentic process workflow. The candidate solution, agentic process workflow and agent compatibility rules are then outputted on a user interface of a user device.