Privacy-Gated Multi-Agent AI Architecture Without Central Orchestration
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Solution Overview
Problem
Centralized orchestration architectures in large language model (LLM) services pose privacy and data residency risks, enforce rigid compliance mechanisms, create scalability bottlenecks, and limit transparency and auditability, complicating adherence to stringent data protection regulations.
Innovation Solution
A decentralized, privacy-gated multi-agent architecture where user messages are anonymized at a Conversation Hub, domain-specific expert agents autonomously assess relevance and compliance, and modular compliance modules enforce tenant-specific policies without central orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized orchestration architecture is used, then system coordination and task management are simplified, but privacy risks and data residency concerns increase
Solution Approach 1:
The system segments the centralized orchestration function into distributed event-driven components. Instead of a single central orchestrator, multiple independent event-driven agents process tasks autonomously based on published events, eliminating the privacy bottleneck while maintaining coordination capability.
Solution Approach 2:
The patent introduces an event bus as an intermediary layer between agents and data sources. Events are published to the bus with necessary information, and agents subscribe to relevant events without direct access to underlying data sources, providing privacy-preserving mediation.
2Stability of the object's composition
If centralized compliance enforcement is implemented, then policy consistency is maintained, but system scalability and adaptability are reduced
Solution Approach 1:
Compliance enforcement is distributed to local event-driven agents, each capable of independently evaluating and enforcing compliance rules for its own operations. This localizes compliance quality while allowing the overall system to scale and adapt through addition of new agents.
Solution Approach 2:
The event bus provides universal event publishing and subscription capabilities that work across all agents and domains. This universal interface enables both policy consistency through standardized event formats and system scalability through flexible agent addition.
3Ease of operation
If monolithic LLM architecture is used, then model output control is centralized, but transparency and auditability are limited
Solution Approach 1:
The monolithic LLM output control is segmented into distributed event processing across multiple agents. Each agent independently processes events and generates outputs that are published back to the event bus, creating transparent, auditable trails for each decision while maintaining overall output control through event-based coordination.
Data Source
AI summary
A privacy-preserving, industry-agnostic artificial intelligence architecture is disclosed. The architecture features a Conversation Hub receiving end-user messages, performing lightweight natural language processing, removing personally identifiable information (PII), securely storing permissible user profile data, and publishing anonymized envelopes onto a decentralized message bus. Independent, stateless Domain Expert Pods, each representing specialized knowledge domains, autonomously subscribe to the bus and determine message relevance. Pods requiring additional user information issue structured requests relayed securely to users by the Conversation Hub. Updated user responses enrich profile data and trigger rebroadcasts. Relevant Domain Expert Pods generate domain-specific responses evaluated by Compliance Pod sidecars enforcing tenant-specific regulatory and policy constraints. Compliance-approved responses are emitted directly onto a client-visible channel, enabling parallel multi-domain expert interactions. Eliminating central orchestration, the architecture provides decentralized expertise selection, adaptive personalization, robust privacy enforcement, and hot-swappable compliance, significantly simplifying integration and enabling rapid policy modifications without altering core logic.