Spectral Whitepaper

Whitepaper

Spectral: An Onchain Agent Economy for Web3

Abstract

This document introduces Spectral, a platform designed to integrate autonomous onchain Agents with the Web3 ecosystem through innovative AI and ML technologies. Spectral's flagship offering is Spectral Syntax, a platform that enables users to create onchain Agents by translating natural language intents into executable code. Agents created through Syntax are compatible with the Inferchain, a new execution, communication and ownership protocol being developed by Spectral Labs that facilitates engagement between multiple AI Agents across chains. With Spectral Syntax, users can easily create onchain Agents from simple instructions and monetize their creation by letting other Syntax users interact with their agent. The Inferchain ensures these Agents can communicate with each other and market participants can engage in owning and operating these agents. The agentic workflows built by design into Spectral Syntax will lead to a thriving onchain agent economy on the Inferchain, which will fundamentally transform the way average retail users interact onchain by letting users create AI Agents that handle complex onchain interactions.

Contents

Introduction
Agent Economy
Spectral Syntax
Future Enhancements and the Inferchain
Token Economy
Conclusion

1. Introduction

Most machine learning (ML) models deployed in production for critical use cases today are built by a handful of large, centralized players with proprietary model training techniques. They’re black boxes. Applying them in a smart contract means relying on a single source of truth and creating a single point of failure.

On the other hand, with the frontiers of innovative AI technology expanding on a daily basis, particularly generative AI, it seems inevitable that AI Agents -- automated code performing tasks on our behalf -- will soon be a mass adopted reality. Many AI innovators, like OpenAI, have already signaled the creation of Agent Marketplaces. Specifically in Web3, AI Agents have massive potential to improve the speed and experience of onchain operations. However, the above mentioned problem of black box models becomes increasingly critical in this context. The decentralized ethos of Web3 clashes with the idea of centralized, opaque marketplaces for the creation and operation of on-chain Agents. Looking ahead to a future where on-chain AI Agents interact autonomously, such closed ecosystems could obscure the origins and efficacy of information shared between Agents, fostering a degree of centralization at odds with the foundational principles of Web3 and potentially leading to unforeseen consequences.

Spectral is solving this problem by bridging the gap between AI, ML and Blockchain, through its products that empower the users to create onchain Agents that operate in a trustless, decentralized manner through a common provenance layer known as the Inferchain. The core product offering, Spectral Syntax, is a network of Onchain Agents. Syntax allows users to create their own onchain Agents through a Solidity co-pilot that understands natural language intents and converts them into code based Agent instructions. Users can create and and monetize their own Agents, or use the Agents created by the community to run their daily Web3 tasks.

The Agents created on the Spectral Syntax network will be integrated through the Inferchain. The Inferchain is Spectral’s vision for the future of Agent to Agent communication, allowing transparency, decentralization and performance verification for AI’s application in the Web3 space.

The below sections attempt to explain our vision for the Agent Economy, illustrate the workings of Spectral Syntax, and ultimately explain incentivization and governance mechanisms operated through the platform’s native token, SPEC. The paper ends with a note on the planned future work, including the long term vision for building the Inferchain.

2. Agent Economy

A. Definition and Features of an Onchain Agent

Since the launch of ChatGPT in Nov 2022, the term “AI Agent'' has become widely used in both the Web3 sector and the broader technology industry. Given the early stage of development, and the emerging ways of implementing an AI Agent in Web3, we find it crucial to lay out our definition of an AI Agent.

AI Agent, in the context of Spectral, is a set of onchain instructions and code that can execute itself autonomously through a provisioned, dedicated wallet. It’s important to note a few key distinctions here:

The Agent has access to its own wallet and private keys allowing it to execute transactions, authenticate signatures and perform additional actions onchain. The Agent performs the authorized set of actions approved by the user on a repeating cadence, without asking the user to approve in wallet every single time.
The set of instructions can be executed consistently. The set of instructions are the identity of the Agent, and the Agent acts predictably in accordance with them.
The Agent is designed to operate autonomously on the blockchain, leveraging real-time data and events to inform its actions. It possesses the capability to analyze incoming information, assess current market conditions, or respond to specific triggers without direct human intervention. This enables the Agent to make independent decisions, whether it's executing trades, adjusting strategies, or performing tasks based on predefined criteria or algorithms. By doing so, it can adapt to changes swiftly, optimize operations for efficiency, or capitalize on opportunities the moment they arise. This level of autonomy ensures that the Agent can effectively manage tasks and make informed decisions in a dynamic environment, aligning with its programmed objectives and the interests it serves.

B. Use Cases for an Onchain Agent

At Spectral, we hold a core belief that AI Agents will eventually become pervasive in our world, shaping our daily lives in profound ways. The rapid advancements in Large Language Models (LLMs) are accelerating us towards this future. Within the Web3 domain, AI Agents, as previously described, offer significant utility across a variety of tasks, enhancing daily operations. Here are a few examples to illustrate this point:

Agents that process transactions: use cases involving complex transaction sequences, such as retail swaps, options settlements, automated liquidations in lending protocols, etc. can be handled by an Agent.
Agents that construct code components: these Agents can create specific code components by looking at an existing projects repository. E.g., Agents that write ERC4337 smart wallet abstractions, Agents that write zkML (zero-knowledge machine learning) circuits, etc.
Agents that carry out trading operations: these Agents can carry out entire sequences of asset pair trading based on an instruction set, and execute profitable strategies for NFTs, currency pairs, arbitrage opportunities, etc.
Agents that carry out onboarding tasks: in these use cases, Agents can be trained on documentations of Web3 infrastructure providers and can automatically onboard users by calling the required APIs to create instances for a new user.

C. Economic Effects and Collective Intelligence

We anticipate the concept of AI Agents evolving similarly to App Stores, where users are naturally motivated to engage with Agents already developed by the community. The community will explore new use cases for creating additional Agents. However, a unique parallel is emerging for Web3 and its AI Agents, distinguishing it from the Web2 model:

Open monetization network: instead of gated, price controlled communities like app stores, we believe in an open network where anyone can create an Agent and monetize its interaction based on their prerogative. Open networks will incentivize for performance and sensitivity of the AI Agent, and hence the interactions with the best Agents will automatically sell at the market discovered price point.
Onchain operations: unlike black boxed models and their APIs, Web3 AI Agents by design can be transparent and measurable by representing their actions on chain. We envision a future where every incoming request to an Agent, the Agent's computation over that request, and its response - everything is fully traced onchain, thus assuring any party of the Agent's proof of execution.

Due to open monetization and operations that are visible onchain, we also believe that AI Agents will possess collective intelligence: they will have the incentives necessary to interact with each other (determined by market forces) and exchange knowledge while being aware of each other’s intelligence.

3. Spectral Syntax

A. Overview

Spectral Syntax is a co-pilot software that helps users create their own Onchain AI Agents. A range of Large Language Models (LLMs) fine tuned on Solidity help Syntax generate functional code that can be used as a set of instructions to create onchain Agents. Through its conversational interface, Syntax can help build a variety of onchain Agents, and also help discover Agents built by the community. Thus, Syntax helps in building the onchain Agent economy by incentivizing the process of creating and interacting with onchain Agents.

B. Technical Components

A variety of technical components power the workflows beneath the Syntax product interface:

Base Models: Spectral uses several SoTA LLMs and applies rigorous fine-tuning techniques to enable Syntax to create agent instructions, as well as utilizing Retrieval Augmented Generation (RAGs) to efficiently embed, within the agent instructions, built-in functions to interact onchain and access a variety of APIs as defined in the plugin manager.. DeepSeek and GPT are the model versions used in the first Syntax release and in inferchain will allow users to integrate other SoTA models in a plug-and-play fashion. All candidate models utilize one or more of the following approaches (Parameter-Efficient Fine-Tuning, Quantized Low-Rank Adaptation, Retrieval Augmented Generation, and DeepSpeed techniques) to improve upon the publicly available models. Depending upon the user intent, one or more of these models will generate a response from the requested AI Agent.
Agent Identities: These identities serve to distinguish one agent from another within a group utilizing the same Large Language Models (LLMs), significantly influencing an Agent's functionality and behavior. This includes the quality and effectiveness of its responses to prompts. An Agent's identity outlines its core behaviors and establishes its role. For instance, an Agent identified as "designed to query the blockchain" will provide details from an Ethereum address when given a .eth address. Behind the scenes, each Agent identity, described in natural language, corresponds to a set of declarative or imperative instructions (such as Python or simple API calls) stored in Syntax's short-term memory. We plan to introduce an Agent Naming Service (ANS), a blockchain-based universal identifier that enables the recognition and tracking of an AI Agent's activities. This system aims to mirror the Ethereum Name Service (ENS), allowing each agent to be identified by a natural language identifier (similar to a domain name) to facilitate interactions.
Agent Knowledge Bases: Along with an Agent’s Identity, a user can provide various knowledge bases which the Agent can refer to while responding to a particular prompt. Some of the knowledge bases are purely textual matter, but can also comprise of URLs. In the backend while processing the prompt, these knowledge bases are passed as in-context information, and thus help the Agent retrieve details if necessary.
Plugins: Agents access plugins to connect itself with the internet and perform various actions. For example, nearly all data sources an Agent needs access to for running functional onchain contracts (e.g. Chainlink oracles for price data feeds, Google for general internet access, etc.) are all plugins which can be called upon by the Agent. Furthermore, the entire Foundry architecture, which is used to deploy contracts onchain, is also deployed as a plugin that is called upon by the Agent in specific compile and deploy operations. The engineering established here is that the Agent not only generates the code, but is also responsible for generating instructions which can be used by Foundry to process and deploy the code onchain.
ML Inferences: Similar to how plugins work, Machine Learning (ML) models can utilize inferences produced by a diverse range of models. By tapping into these ML inferences, Agents can automatically input target variables and retrieve the corresponding inferred values from the model, streamlining the process of leveraging advanced ML capabilities.
Wallet Management: A strong distinction of an onchain Agent is the ability for it to be able to deploy code autonomously. Spectral Syntax makes such dedicated wallets available to its Agent by creating a ERC4337 Smart Wallet Abstraction between the User and Syntax. This smart wallet acts as the Agent’s own wallet, which can be used by the Agent to sign transactions, handle assets, and pay for gas usage. Although the Agent acts autonomously, the user is in control at all times, because the user controls the amount of funds to hand over to the Agent’s wallet. Alongside Agent Wallets, users will have their own self custodial wallet and a Gas Tank associated with their wallet. The gas tank pays for gas and platform usage fees associated with every transaction, and the segregation between the agent wallet and gas tank ensures that the user does not need to move around assets in order to cover their gas fees. This allows users to scale their transaction volumes by pre-filling their gas tank with nominal funds that can cover the costs of transacting on efficient L2s.
Execution Engine: behind the scenes, this engine handles the execution of all agent instructions, which can include monitoring different events from the various knowledge bases and plugins. Each instruction leads to an action, called triggers, such as conducting transactions like buying or selling ETH. The agent instructions being executed are control flows that can be read from various data sources managed by the Plugin Manager.

C. Workflows

Spectral Syntax can allow users to create onchain Agents because it combines the generative power of LLMs with an onchain contract deployment architecture. This workflow, which combines the prompt and inference from an LLM with Foundry architecture on demand is behind the seamless interaction behind all onchain Agents. We explain here the sequence in which these interactions happen:

General Architecture
Diagram: General Architecture of the Spectral Syntax Network
Spectral Syntax is built on an intricate engineering architecture which provides LLMs seamless access to onchain infrastructure. Following are the various components used in the network:
- Orchestrator: The Orchestrator is Spectral's proprietary backend service, which directs communications between system components. Internally, this proprietary backend service is called Backend Resource for Optimal Service (bros). On top of interfacing with the user via Syntax’s frontend, it serves as the communication layer that forwards user prompts to Agents, routes user requests to necessary plugins, and engages the Deployment rails. The Orchestrator also invokes the Wallet Manager to seamlessly deploy contracts onchain.
- Wallet Manager: This component connects the Agent and LLM models to user wallets and prompts them for signatures during code deployment. The Wallet Manager is also responsible for generating Agent Wallets using smart wallet account abstractions. With a self wallet and Agent’s wallet, a user will be empowered to transfer a set of funds to their Agent, and allow their Agent to use them for autonomous transactions.
- Agent Handler: The Agent Handler manages communications with multiple Agents, contextualizes prompts, and directs specific requests to the appropriate Agent. It also oversees the creation of instructions for plugin invocation and code tests to assess Agent-written code's efficiency.
- Agent Network: Spectral Syntax network consists of multiple Agents, depending on the identities of and use cases for individual Agents. Depending upon the user’s selection, the Agent handler can select and call upon a particular Agent in a conversation.
- Plugin Manager: This component is responsible for directing instructions to the right plugins and relaying responses to Agents. Plugins, in the context of the Syntax network, are a broad category of APIs, including pricing oracles like Chainlink, third party APIs like DeFiLlama and Google, inference feeds from decentralized ML models, and deployment rails like Foundry - all these APIs can be called upon just-in-time, depending upon the requirement of the prompt.
- Modular Deployment Rails: Using Foundry, an open-source toolkit for Ethereum development, Spectral orchestrates the compiling, testing, and deploying of Agentic code over EVMs.
To clarify the roles and implementation details, we will refer to the example of an AI trading agent illustrated in the following “Detailed Technical Architecture” diagram.
Diagram: Detailed Technical Architecture for onchain trading agents
- API Endpoint: Acts as a conduit for external interactions.
- Syntax LLM: This module aids in the configuration and decision-making processes by leveraging language models.
- Agent Simulation (Forward Testing): Facilitates testing of agent configurations before deployment.
- Agent Configuration: Stores configuration data for the trading agents.
- User Management: Manages user-related data and settings.
During the process of executing agent instructions, after an event trigger has been observed and an agent concludes to conduct an onchain interaction (such as a transaction), the onchain signing process begins. The onchain signing process is facilitated by the Alchemy SDK, which requests Turnkey to sign the action on behalf of the agent. Following the signing, the transaction is sent to the Alchemy paymaster, which is responsible for executing the onchain transaction.
Listen, Evaluate, and Act (LEA) Model
Every agent on Syntax is associated with a set of instructions (namely, the agent instructions). These agent instructions are primarily stored as python code. The diagram below illustrates the approach from the initial user interaction to the final agent instruction creation:
Diagram: LEA Model Workflow
The process starts with the user’s input triggering a Q&A session with the agent builder. This session assists users in helping build their agent. Once the user provides all of the required information about their agent, the process of segmentation begins. This process segments the user’s prompts and collected agent information into the category of: Listen, Evaluate, and Act functions. The segmented prompts are then fed into the Code Generation phase, where RAG is applied to utilize any of the built-in functions or APIs such as interacting with onchain functions. This phase also includes an LLM that conducts Testing and Code Evaluation, which is crucial for ensuring the functionality and reliability of the generated code. It includes: static analysis, running, and debugging the code, conducting unit tests for types passed between functions, and critiquing the code using LLM to improve its quality. Upon completion, this finalizes the LEA model and generates the agent instructions which are now ready to be executed through the job builder.
Architecture Sequence Diagram
Diagram: Sequence Diagrams of various flows
This sequence diagram provides a detailed overview of the various workflows between the user, agent, and imperative components. The system integrates multiple components, including an Identity Platform, Syntax UI, various "bros" modules (API, Base LLM, and Execution Engine), the Plugin Manager, the Agent Wallet Portal, Database, and the Blockchain. The diagram illustrates the steps from user authorization to the execution of onchain transactions by the trading agents. The user to agent interaction and agent deployment sequence is detailed further in the next section.
Prompt Generation and Deployment Sequence
Diagram: Prompt Generation and Deployment Sequence
When a prompt is received from the user, the request travels through the various components within Spectral Syntax in the following manner:
1. Step 1: User first inputs a prompt into the interface for Spectral Syntax or one of the pre-built Agents.
2. Step 2: Orchestrator receives the user prompt and prepares it for processing. It utilizes a Coordinating LLM to generate structured instructions based on the prompt, according to the context and intent, and decides to route the request to a particular Agent.
3. Step 3: Depending on the instructions provided by the Coordinating LLM, the Orchestrator searches for the appropriate Agent Schema in the local network directory of Agents. This schema includes information about the identity of the Agent, available actions, entities and data sources related to a particular Agent.
4. Step 4: Orchestrator then calls the Agent handler, and passes the structured instructions and the relevant Agent schema to the LLM.
5. Step 5: Agent Handler communicates with the relevant components or APIs to fulfill the user request, such as querying databases or external services. Agent handler also requests additional contextual data from the Vector DB (containing model embeddings) and then constructs an enhanced prompt which needs to be sent to the LLM for processing.
6. Step 6: Agent Handler initiates a call to the Language Model (LLM) with the enhanced prompt, Agent schema, and additional contextual data. LLM generates a response using natural language understanding and generation techniques. The response is crafted to address the user's query or request, leveraging the provided context and available information.
7. Step 7: The Orchestrator analyzes the response, and depending upon the processing of the prompt decides if Plugin Manager needs to be invoked.
8. Step 8: Plugin Manager invokes relevant plugins based on the response sent by the LLM. Plugins may perform tasks related to a variety of use cases: fetching a price from an oracle, executing a compile instruction, deploying a code onchain, etc.
9. Step 9: The Orchestrator decides if it needs to augment the processing result with additional data - if necessary, it will repeat Steps 2-9 until it processes the full prompt. Once completed, the Orchestrator then collates the outputs from the LLM and executed plugins to form a comprehensive response that’s suitable for user’s consumption.
10. Step 10: After ensuring the response is coherent, relevant, and actionable, it delivers the response to the user through the platform interface, completing the prompt processing flow.

4. Future Enhancements and the Inferchain

The above paper has explored the design, workflows and components behind Spectral, an ecosystem bridging the gap between AI, ML and Blockchain through the Syntax network.

Near term roadmap for Syntax will focus on empowering people to enrich their engagement with the Agent Economy.

Ability to create your own agents: users will be able to create their custom agents and modify their identities, knowledge bases and plugin access. This feature will lead to the creation of a variety of agents for different tasks in Web3.
Roster of industry grade plugins: To enable complex use cases and end to end functionality, we are currently engaging with several providers to enhance availability of oracles, feeds, etc. to Agents on the Syntax network.
Monetizing Agent interaction: Users will soon be incentivized to create Agents and can set a monetary transaction mechanism for allowing other users to interact with their Agent. This feature will enable Syntax to thrive as an open marketplace for Web3 AI Agents, thus creating network effects and fueling more Agent creation, engagement and adoption.
B2B Agents: Spectral is also engaging with several chains, tooling providers, and DeFi protocols to investigate creation of B2B agents for specific use cases. These Agents will help our partners to easily onboard their users without existing UX friction, and amplify their growth metrics.

Building on top of our existing community strength, our future roadmap is geared towards The Inferchain, which is envisioned as a decentralized protocol that will enable the ownership, creation, and operation of AI agents on-chain through a fully trustless framework. The architecture of Inferchain will segregate ownership and operational responsibilities, allowing different entities to manage these aspects independently, thus promoting decentralized control and minimizing potential conflicts of interest.

Each AI agent within the Inferchain ecosystem is anticipated to be structured around four key components:

Privacy-Enabled Knowledge Base: This component will store the agent's proprietary or publicly accessible data, ensuring privacy and security. The knowledge base is expected to be accessible only to the agent's owner or authorized entities, serving as the foundation for the agent's decision-making processes.
Verifiable Smart Contract Instructions: The core logic and behavioral instructions of the agent will be encoded in smart contracts. These instructions will be immutable and publicly verifiable, providing transparency and trust in the agent's operations. The smart contracts will dictate how the agent interacts with the blockchain and other external entities.
Executioner Node: The Executioner Node will serve as the interface between the agent and external services, including plugins, decentralized applications (dApps), and other blockchain networks. This node will be responsible for executing the agent's tasks, processing user requests, and ensuring the agent's operational integrity across various platforms.
Agent Name Service (ANS): The ANS will act as a global registry for AI agents, providing unique identifiers that will allow each agent to be consistently addressed across different chains and protocols. This service will enhance the interoperability and accessibility of agents within the Inferchain ecosystem.

Ownership of agents on Inferchain will be defined through ERC20 token shares specific to each agent. These tokens will represent ownership stakes and will incentivize decentralized ownership among community members. The distribution of these tokens will enable collective development and enhancement of the agents, fostering a community-driven approach to AI agent evolution.

Operators, or Executioners, of these agents will be incentivized to maintain and operate the nodes by staking $SPEC tokens. This staking mechanism will serve as a security measure, aligning the interests of the operators with the overall network. In return for their services, operators will earn transaction fees generated by the agent, creating a sustainable economic model for decentralized agent operation.

By implementing this architecture, Inferchain will aim to significantly lower the barriers for creating and managing AI agents onchain. Leveraging advanced incentive mechanisms, Inferchain will simplify complex onchain interactions and drive value creation for a broad range of users in the Web3 ecosystem.

With the aforementioned initiatives in sight, we’re focusing our 2024 efforts on the following product roadmap:

Inception (Q1) focuses on launching the Spectral Syntax network, including decentralized architecture and SPEC governance.
Scaling (Q2) aims to grow network usage with advanced features and ability to do more with Agents and monetize them on Spectral Syntax.
Diversification (Q3) will focus on enriching the Spectral Syntax network with complex AI Agents capable of end to end interactions, developed in partnership with several other Web3 firms.
The final phase, The Inferchain (Q4), aims to actualize the Agent Economy by optimizing universal Agent identification, creation, ownership and operations.

5. Token Economy

A. Overview of the SPEC Token

At Spectral, we are pioneering the Onchain Agent Economy with Syntax, enabling users to create and operate powerful onchain agents that transform how they interact within Web3. Through natural language conversations, users can delegate complex tasks to their agents, streamlining their onchain activities. To accelerate the adoption of agentic technology, we’re implementing a robust tokenomics model designed to foster organic network effects and incentivize participation on the SYNTAX network. These incentives are intricately aligned with our long-term vision, ensuring sustainable growth and engagement within a thriving ecosystem of Onchain Agents.

Central to this vision is the SPEC token, an ERC20 governance token that empowers community stakeholders to actively participate in the network's decision-making processes. SPEC enables users building agents on Spectral Syntax and the Inferchain to propose and vote on platform upgrades, modifications, and parameter adjustments, ensuring a decentralized and inclusive approach to governance. Beyond governance, SPEC serves as a utility and value exchange mechanism, integral to the monetization and operation of agents within the network.

B. Incentive and Utility Mechanism

The Syntax network is a two-sided marketplace between Creators, Users and Agents. Creators create the Agents and publish them on the Syntax network. Creators can use Syntax’s AgentBuilder to build agents through natural language instructions (note that creators can also be Users of other Agents on the network). Users initiate tasks (jobs) for the AI agents available on the Syntax network. Agents understand the request from their users, then compose an execution code (python script) that allows them to perform the actions autonomously over a user-specified timeframe.

These three actors come together to create a vibrant network effect on Syntax that in turn reinforces the incentive mechanisms that enable these actors to accelerate agent adoption. Here’s how this mechanism works:

Diagram: Incentive and Utility Mechanism on Spectral Syntax Network

User-Agent Interactions: Users pay Transaction Fees and Usage Fees for using the Agents on the Syntax network, and Agents utilize these funds to facilitate the execution of jobs created by the user.
Creator-Agent Interactions: Agent Creators on the Syntax network receive ongoing Creator Earnings, defined for every transaction done by their agent while serving users, as a percentage of the transaction value. More efficient, powerful agents are likely to secure more users and conduct more transactions, and therefore a Creator is incentivized to keep improving their agents to serve more users.

C. Staking Benefits

Spectral’s Governance token, SPEC, is employed in our tokenomics as the self-sustaining mechanism for value distribution across the network as follows:

Users can stake SPEC to receive a discount on their transaction fees. This mechanism allows users to secure increasing yield as their engagement in agentic trading and other onchain activities increases.
Agent Creators can stake SPEC to gain higher percentages of creator earnings against every transaction made by their agent. This incentive allows creators to invest efforts continuously into improving the technical capabilities of their onchain agents. The tokens staked by the creator derive more yield as a creator’s agent serves more users.

To further encourage adoption of the agentic paradigm, we may bring forward governance proposals for enhanced incentive mechanisms that improve utility for SPEC community members. Such proposals will aim to encourage Agent Creators to invest time and effort in the continuous improvement of their agents, thus amplifying the overall value created on the Syntax network.

D. Governance

SPEC token serves as a powerful mechanism for actors in the system to actively participate in the governance of the Decentralized Autonomous Organization.

Voting on Network Improvement Proposals: SPEC token holders can actively participate in the governance process by submitting and voting on proposals related to network upgrades, feature implementations, fee structures, and standardized challenge rules. While proposing proposals and voting on them, the DAO shall follow the following guidelines:
- Voting power is proportional to the amount of SPEC held by a user.
- There is a designated voting period for each proposal.
- Token holders express support or opposition during the voting period.
- Votes can be cast directly or delegated to other addresses.
- Delegating voting power promotes broader participation.
- A proposal must meet a minimum quorum requirement to be valid.
- Approval requirement is typically 40% of total votes for a successful proposal.
- If a proposal meets both the quorum and approval requirements, it is considered successful. The proposed changes are then implemented in the protocol according to the terms outlined in the proposal.
Influencing Governance Parameters: Token holders have the ability to propose and vote on adjustments to governance parameters, including voting rules, quorum thresholds, and voting periods, contributing to the agility and responsiveness of the platform's governance model.
Staking for Agent Operations: On Spectral Syntax, staking gives users privileged access for monetizing the Agent network. To that end, SPEC holders can use their tokens to influence the manners in which Spectral monetizes the Agent network, and propose platform fee changes, suggest strategic partnerships, modify content moderation rules, etc.
Participating in Smart Contract Upgrades: Through DAO governance, SPEC token holders can vote on proposed changes to Spectral's smart contracts. This allows for the platform's flexibility and adaptability to emerging technologies and security enhancements.
Allocating Funds for Community Initiatives: The DAO may allocate a portion of the platform's funds to support community-driven initiatives, research and development, marketing efforts, or partnerships. This mechanism empowers the community to drive initiatives that benefit the platform as a whole.
Shaping the Future of Spectral: Overall, holding SPEC tokens grants individuals the opportunity to actively shape the future of the Spectral Network by participating in various governance activities, ensuring a transparent and inclusive decision-making process.

6. Conclusion

In this whitepaper, we have outlined the ambitious and transformative vision of Spectral, a platform poised to revolutionize the intersection of AI, ML, and blockchain technology through its flagship offering, Spectral Syntax. By enabling the creation of autonomous onchain Agents that operate in a decentralized, trustless manner, Spectral is addressing the inherent challenges of centralization and opacity that currently plague the AI and blockchain industries.

Spectral Syntax empowers users to create, monetize, and interact with AI Agents that can execute complex onchain tasks, significantly enhancing the efficiency and transparency of Web3 operations. The integration of these Agents with the Inferchain, our novel execution, communication, and ownership protocol, ensures seamless interaction and collaboration between Agents across different chains, fostering an interconnected and thriving onchain Agent economy.

The SPEC token serves as the backbone of this ecosystem, facilitating governance, incentivization, and value transfer within the network. By staking SPEC, users gain privileged access to Agent creation and monetization, reinforcing the network's integrity and promoting the development of high-quality Agents.

Spectral Syntax is positioned to be a catalyst for a new era of decentralized intelligence. By bridging the gap between AI, ML, and blockchain, Spectral is empowering users to create exponential value onchain and paving the way for the next billion users to join the Web3 revolution.