AI Resource Token Allocation for Multi-Agent Workflow Bottlenecks
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Solution Overview
Problem
Traditional AI resource allocation methods in multi-agent systems are inefficient due to their inability to adapt to dynamic workloads, lack cost-effectiveness evaluation, fail to incentivize resource conservation, are susceptible to central failures, and struggle with scalability, leading to suboptimal implementations and budget overruns.
Innovation Solution
A token-based economy for resource allocation, incorporating priority queuing and peer-to-peer sharing, with a distributed ledger for transparent record-keeping, enabling market-driven incentives and efficient resource utilization across multiple AI agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional centralized resource allocation methods are used, then resource management is simplified, but the system cannot adapt to dynamic workloads and is susceptible to central failures
Solution Approach 1:
The patent segments the centralized resource allocation system into multiple autonomous agents that operate independently. Each agent manages its own resource requests and makes allocation decisions based on local policies, eliminating the single point of failure and enabling adaptive responses to dynamic workloads without requiring a complex centralized controller.
Solution Approach 2:
The patent implements self-service mechanisms where computational agents automatically evaluate their own resource needs, submit requests, and receive allocations based on their priority levels and available tokens. This autonomous self-management enables dynamic adaptation to workload changes while reducing the complexity of centralized control systems.
2Productivity
If static resource allocation is used, then system management is simpler, but resource utilization is inefficient and leads to budget overruns
Solution Approach 1:
The patent implements dynamic resource allocation where the system continuously adjusts resource allocations based on real-time workload conditions, agent priorities, and token availability. This dynamic approach optimizes resource utilization efficiency by allocating resources to the most urgent and valuable tasks while adapting to changing system conditions, preventing budget overruns through automated cost tracking.
Solution Approach 2:
The patent incorporates feedback mechanisms where agents receive information about their resource consumption, priority levels, and allocation outcomes. This feedback enables agents to adjust their resource requests and priorities dynamically, improving overall resource utilization efficiency while maintaining manageable complexity through automated decision-making rules.
3Speed
If high-priority resource requests are expedited, then critical tasks are completed faster, but fair access to resources is compromised
Solution Approach 1:
The patent applies local quality by differentiating resource access characteristics based on agent priority levels. High-priority agents receive expedited resource allocation and faster access speeds when needed, while lower-priority agents receive standard allocation. This differentiated approach maintains fairness by ensuring each agent type receives appropriate service levels while still enabling critical tasks to complete faster.
Solution Approach 2:
The patent changes the allocation parameter of resource access speed based on agent priority levels and system conditions. High-priority agents can obtain resources more quickly through expedited allocation mechanisms, while the system maintains fairness by ensuring lower-priority agents still receive their required resources through standard allocation processes, balancing speed and fairness through parameter adjustment.
Data Source
AI summary
Systems, methods, and devices for facilitating computational resource access by artificial intelligence (AI) agents through token-based allocation and multi-agent workflow optimization. The system generates tokens corresponding to computational resources including processing power, memory, storage, and bandwidth. AI agents submit resource requests with priority tokens, creating queues ordered by priority token quantity. Higher-priority bids receive preferential positions. The system transfers resource tokens to agents based on queue order, enabling resource access through token exchange. The system can receive user prompts indicating computational objectives and determines multiple AI agentic approaches comprising AI model sequences. After evaluating approaches against operational policies, the system generates resource utilization and performance estimates, executing a preferred approach balancing efficiency with output quality. All transactions, including participating agents, token transfers, selected agentic approaches, and estimates, are recorded on a distributed ledger for transparency.