Agent I/O Transformation for Secure Fault-Tolerant AI Communications
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Solution Overview
Problem
Current AI generative models and LLMs require super-computing efforts to improve response times, accuracies, and reduce computational load, and lack comprehensive fault tolerance mechanisms, posing risks to reliability and scalability in mission-critical scenarios.
Innovation Solution
A system and method for fault-tolerant generative AI applications using derived requests, multiple h-LLMs, local databases, distributed architectures, shadow agents, checkpointing, state saving, message pool management, failure detection algorithms, and flexible agent replacement mechanisms to ensure continuous operation and reliability in multi-agent systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If super-computing efforts are used to improve response times and accuracies, then the performance of generative AI models is improved, but the computational load increases
Solution Approach 1:
The patent segments the generative AI system into multiple specialized h-LLM models, each trained on specific datasets for particular tasks. This segmentation allows the system to distribute computational load across multiple smaller models rather than relying on a single large model, improving both accuracy for specific tasks and computational efficiency by selecting the most appropriate model for each query.
Solution Approach 2:
The system dynamically changes parameters by selecting different h-LLM models based on task requirements, dataset characteristics, and performance needs. This parameter change approach allows the system to optimize the balance between accuracy and computational load by choosing the most efficient model configuration for each specific task rather than always using the most computationally intensive option.
2Reliability
If fault tolerance mechanisms are added to multi-agent systems, then reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training multiple specialized h-LLM models on different datasets before deployment. This preparation ensures that when failures occur in a multi-agent system, alternative pre-trained models are already available to take over, providing fault tolerance without requiring complex real-time decision-making or retraining mechanisms during operation.
Solution Approach 2:
The system creates copies of agent functionality through multiple specialized h-LLM models that can substitute for one another. Each model serves as a copy of the core reasoning capability but specialized for different domains or tasks, allowing the system to maintain reliability through redundancy while keeping individual model complexity manageable.
Data Source
AI summary
Systems and methods for securing agent communications through input/output transformation including generating a secured input including an input normalizer or a context enricher, deploying an output adapter including an abstraction layer and an output formatter, generating the secured input by processing a received input by the input normalizer or enriching the normalized input by the context enricher, routing the secured input to one or more agents for processing, generating an abstracted output by abstracting agent output by the abstraction layer, generating the formatted output by formatting the abstracted output by the output formatter according to one or more security policies, and transmitting the formatted output to one or more external systems.