NuNet’s APIs will support the functionalities of decentralized computing platforms and marketplaces, initially of SingularityNET and members of the Decentralized AI Alliance (DAIA). These functionalities include, but are not limited to:

• Mobile Computations Processes

• Flexible Workflow Design

• Data and Value Production & Exchange

• Logical Scalability

• Verification and Validation

• An Ecosystem of Adaptive Decentralized Computations

• Learning and Meta-Learning

• Human-mediated Cognitive Development

Agents are building blocks that can be combined to form arbitrarily complex domain specific computing workflows that can perform a variety of useful computations in the network. The same agent can participate in many workflows, and connect with other agents to form clusters. Agent mobility enables such workflows to operate across boundaries of cloud vendors, mobile devices, private clouds and more, while respecting and ensuring ownership, economic value of resources, and data security/privacy are maintained by the respective parties. NuNet implements a tokenomic mechanism to enable and facilitate the design of frictionless cross-vendor workflow execution.

In terms of workflow design, agents, using NuNet’s functionality, will be able to search for other agents in the network, which could provide building blocks for their original task, calculate the costs of such workflows, and estimate time requirements of execution. This would allow for agents to make optimal decisions with or without help from humans, and enable agents to express larger computational tasks that would be difficult for one agent to achieve.

The workflow execution aspect of computational reflection will enable agents to time, schedule and manage the actual execution of their workflow, data transfers between agents, error propagation, crash recovery, necessary caching, etc.

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.

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.

An obvious requirement and one of the most important aspects of the framework’s functionality is the ability to verify and validate the correctness of computational processes performed in a network and establish a way to validate good users/components, reward good actors and punish bad actors.

The ability to verify and validate each general computational process in a decentralized network can only be performed in a decentralized way, which means that NuNet as a whole will not attempt to provide guarantees of the correctness of each process and computational workflow performed in the network. Instead, the framework will provide APIs, tools, network-wide telemetry information, and reputation system(s) that will enable each constituent of the network (network operations agent) to evaluate the validity and correctness of concrete results of the computational processes in question. Through network-wide telemetry information available to all constituents of the network, NuNet will facilitate the self-learning and healing capabilities of the framework effectively minimising the impact of bad actors on the overall network performance as well as the results of individual computational processes.

Main aspects which will ensure the reliability of the NuNet network and the validity of its individual computational processes are:

1. The tokenomic mechanism supported by implicit and explicit reputation systems, on top of technical means of verification and validation, will provide immediate and clear economic incentives for the good (i.e. beneficial for all) behavior of network constituents;

2. The variant of the non-repudiation/proof of receipt mechanism, where new tokens will be minted and distributed to platform users upon successful completion of a transaction and based on actual computing power used by this transaction - a crucial part of NuNet tokenomics.

3. NuNet will also make the best use of formal third-party verification tools, such as those developed by SingularityNET, TrueBit, zkSNARKs, or other open-source protocols or even businesses, as well as encourage using secure hardware enclaves. However, formal verification methods are an active research field and are not available for verifying computations in general - only in specific cases. Therefore, NuNet will mostly rely on tokenomic and reputation-based mechanisms, while integrating formal work verification methods for specific use cases where it is appropriate. In the future, NuNet will aim to provide an API for integrating third-party formal verification tools for general usage.

NuNet will support the principle of radical decentralization of the computing platforms and marketplaces in the sense that every agent will be able to become a meta-agent if it decides to do so and has computational, cognitive, and financial resources or the support of human operators to execute such roles. Given a large enough number of agents operating in the network, their ability to form workflows on their own will lead to pluripotency and degeneracy (i.e. many-to-many relations of structures and functions), competition, cooperation, and capacity of the network to self-organize into progressively more complex cognitive structures.

In the decomposition of NuNet participants into computational resource providers, computational resource users, and network operations agents, meta-agents may fall into any of the categories; or a single meta-agent might span 2 or 3 of the categories.

Ecosystems of adaptive decentralized computations, whose individual agents are capable of learning and meta-learning in collaboration with each other, will give rise to the learning and adaptive capabilities of the decentralized marketplace of NuNet as a whole. Since some agents will represent humans participating in the network, and in the beginning human agents may contribute the largest part of the intelligence of the network, the framework as a whole will be able to learn from human actions and intelligence and progressively undergo cognitive development. The governance mechanisms of NuNet will guide this evolutionary development for the benefit of all.