Logical Scalability

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Computational reflection of agents, especially in their workflow design and execution aspects, will allow entities to create workflows (i.e. logical structures) in the network in a decentralized manner (see picture below). In a decentralized network, meta-agents can act as intermediaries that transform input data into output data through the curation of other agents’ computational services, which ultimately can be expressed as a logical structure consisting of a variety of agents existing in a connected network workflow. So while such a meta-agent uses the same abstraction as other agents in the network, internally it holds only the computational reflection (or representation) of a workflow: the identities of agents in workflow, their inputs and outputs, their cost, location, and data offered, as well as scheduling information needed for designing and executing a workflow.

Figure 2: (a) Computational workflows will involve computing power and data of various devices enabled by NuNet adapters installed on each device and network operating agents orchestrating them; (b) Meta-agents will be able to create complex computational reflections consisting of a hierarchy of sub-meta agents, all the way down to base agent services hosted on supported decentralized computing platforms, such as SingulairtyNET.

Once the computational reflection is fully mapped out by a meta-agent, the workflow can be executed entirely at their discretion, provided that the initial data and the amount of tokens covering the costs of all computational agents within the workflow are covered. Note that as meta-agents are able to design workflows involving other computational agents, similarly meta-agents themselves can be incorporated into higher-order workflows giving rise to the logical scalability property of the network. Meta-agents will be able to create complex computational reflections consisting of a hierarchy of sub-meta agents, all the way down to base agent services, that are constantly and dynamically changing their costs, workflows, and services offered. Furthermore, these workflows can be designed by a human operator, automatic procedure, or an AI agent using the same level of abstraction. These functionalities will give rise to what call a decentralized network of dynamic service meshes.