Distributed AI Agent Manifests for Scalable Task Delegation

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Solution Overview

Problem

Existing AI agent frameworks are monolithic and lack mechanisms for distributing complex tasks across multiple agents, leading to inefficiencies in performance, scalability, and policy adherence, with no defined method for inter-agent interaction.

Innovation Solution

A distributed AI agent framework that allows agents to communicate through tool calls, abstracting remote agents by encapsulating their capabilities in a manifest, enabling seamless interaction and task delegation between agents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If monolithic agent frameworks are used, then implementation simplicity is maintained, but scalability and performance are limited

Engineering Contradiction:

ImprovescalabilityVSAvoidframework structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent divides the monolithic agent framework into distributed microservices. Each agent is an independent service that can be deployed, scaled, and managed separately. This segmentation enables the system to scale horizontally by adding more agent instances across multiple locations while maintaining implementation simplicity through standardized service interfaces.

Inventive Principle:

Principle #1Segmentation

2Productivity

If tasks are centralized in a single agent framework, then coordination is simplified, but performance and responsiveness deteriorate

Engineering Contradiction:

Improvetask execution efficiencyVSAvoidinter-agent communication mechanism

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces a service mesh as an intermediary layer that manages communication between distributed agents. The service mesh handles service discovery, load balancing, and task routing automatically, allowing agents to execute tasks in parallel across the distribution network without requiring complex direct communication protocols between them.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If agent capabilities are exposed in detail to enable selection, then task allocation precision is improved, but information overhead increases

Engineering Contradiction:

Improveagent selection accuracyVSAvoidcommunication overhead

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent extracts agent capabilities into standardized capability descriptors that are published in service registries. When task allocation is needed, agents query the registry for matching capabilities rather than exchanging detailed capability information directly. This extraction mechanism enables precise agent selection based on task requirements while minimizing communication overhead by using standardized, compact capability representations.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260073257A1Distributed ai agent framework

Publication Date: 2026.03.12 CISCO TECHNOLOGY INC

AI summary

In one implementation, a device that executes a first artificial intelligence agent receives a request to perform a task. The device obtains a manifest that represents capabilities of a second artificial intelligence agent. The first artificial intelligence agent selects the second artificial intelligence agent to perform a portion of the task, by inputting the manifest and an indication of the task as input to an artificial intelligence model. The first artificial intelligence agent sends a request to the second artificial intelligence agent to perform the portion of the task.