Pairing to $EXACT Market

Below is a technical and token-economic explanation of how Exact Agent enables agents to be launched with their own bonding curves, paired against the native $EXACT token. This allows partial ownership in specific agents, as well as flexible cost structures for agent tasks, computation, and storage on the Exact Agent mainnet.

1. Agent Creation & Parametrization

1.1 Agent Specification

1. Goal Setting
2. • The creator (developer or organization) defines the agent’s core objectives, such as monitoring supply chains, automating ticket queues, or analyzing financial data.
   • Goals are encoded in metadata, which includes the domain (e.g., Odoo tasks), input formats, output actions, and success criteria.
2. Task Definitions
3. • Tasks are modular units the agent can perform (e.g., scanning data, generating reports, responding to user prompts).
   • The developer can set parameters such as max tasks per minute or max tasks per hour and permissible data sources.
3. Agent Parameters
4. • Configuration for resource usage (CPU, memory), data pipeline references, or integration endpoints.
   • Security constraints and compliance settings (e.g., requiring private key signatures or KYC for certain data flows).

1.2 Token Creation (Agent Token)

• Agent Token Contract
• • Similar to launching a standard ERC-20 or ERC-1155 token, the creator deploys a smart contract that represents shares in the agent.
  • This contract defines the supply, bonding curve parameters, fee structure, and any special logic for ownership or governance.

2. Bonding Curve Mechanics

2.1 Definition of a Bonding Curve

A bonding curve is a continuous pricing mechanism where the token price is mathematically tied to the number of tokens in circulation (or the agent’s token supply). Common formulas include linear (price ∝ supply) or exponential curves (price ∝ e^(k·supply)).

2.2 Pairing Against $EXACT

1. Liquidity Pool Setup
2. • The agent’s token is paired against $EXACT in a smart contract liquidity pool (e.g., a specialized AMM on the Exact Agent mainnet).
   • When new participants buy the agent’s token, they deposit $EXACT into the pool; when they sell, they receive $EXACT back, with the price set by the bonding curve.
2. Ownership & Governance
3. • Holding the agent’s token confers partial ownership. Owners may receive dividends or revenue shares based on the agent’s activity fees, or have voting rights on upgrades and parameter changes.
   • This structure enables fluid entry and exit: if demand for the agent’s services grows, the token price typically increases, rewarding early backers.

3. Computation & Storage Fees

3.1 Task-Based Fee Model

Each agent on the Exact Agent mainnet incurs a usage fee, payable in $EXACT, based on:

1. Tasks per Minute/Hour
2. • A baseline rate is set per task operation. If an agent is configured to handle up to 100 tasks/hour, fees are computed proportionally to each executed task.
2. Computation Cost
3. • Tasks that involve extensive computation (e.g., LLM inference on large data sets) pay a higher usage fee than simpler tasks.
3. Storage Overheads
4. • Agents storing or retrieving large amounts of data pay additional fees. This might be metered using decentralized storage solutions (e.g., IPFS, Filecoin) or Exact Agent’s internal storage layer.

3.2 Resource Metering & Settlement

• On-Chain Metering: A smart contract tracks each task invocation, referencing resource usage logs or oracles that measure CPU cycles, memory usage, or storage I/O.
• Periodic Settlement: At set intervals (daily, weekly), the agent’s token contract automatically debits the required $EXACT from the agent’s escrow or reserves. If the agent’s balance is insufficient, task execution can be paused, or the system can notify token holders to inject more $EXACT.

4. Lifecycle of an Agent with Bonding Curve

4.1 Launch Phase

1. Smart Contract Deployment
2. • The creator deploys two key contracts:
     (a) Agent Token Contract (defining supply, bonding curve logic, ownership rights)
     (b) Bonding Curve/AMM Contract (facilitating exchange with $EXACT)
2. Initial Seeding
3. • The creator seeds the pool with initial liquidity in $AGTDAO, sets a base price, and mints a certain supply of agent tokens (or allows for continuous minting if no max supply is imposed).

4.2 Growth & Operations

1. Community Participation
2. • Investors or users buy the agent’s tokens if they believe in its future utility, growth, or revenue potential.
   • The agent is deployed to run tasks; as usage increases, more fees flow in, potentially increasing token demand.
2. Revenue Distribution
3. • A portion of fees collected by the agent (in $EXACT) can be distributed automatically to token holders. The distribution model might be pro rata or might require active staking.

4.3 Upgrades & Governance

• Parameter Adjustments: If the community decides tasks/hour should increase or the agent’s LLM model should be upgraded, a governance proposal is submitted. If approved by token holders, the new parameters are encoded on-chain.
• Bonding Curve Tweaks: Over time, the agent’s community may vote to modify the bonding curve formula to better reflect market dynamics.

4.4 Maturity or Deactivation

• Exit Mechanics: If the agent’s mission ends or the community deems it obsolete, token holders can withdraw liquidity in $EXACT by selling back their agent tokens through the bonding curve.
• Agent Dissolution: The underlying contract can be set to “dissolved,” archiving the agent’s data and disabling further tasks. Any remaining funds or assets are distributed to token holders based on final holdings.

5. Key Technical Advantages

1. Scalable Agent Launch
2. • Users can create new agents nearly as easily as deploying an ERC-20 token—specifying goals, tasks, and operational parameters.
   • Bonding curves automate price discovery, eliminating the complexity of setting fixed supply or manual liquidity management.
2. Aligned Incentives
3. • Because owners of an agent’s token directly benefit from its success, they are incentivized to market the agent’s services, refine its underlying pipelines, or provide better training data.
   • Higher usage → more fees → more demand for the agent token → upward price pressure.
3. Resource-Aware Architecture
4. • The pay-per-task model ensures that the Exact Agent mainnet remains sustainable. Highly active agents pay proportionally for their resource consumption, discouraging spam or wasteful computation.
4. Easy Governance
5. • On-chain votes by token holders can quickly alter the agent’s operational parameters (e.g., maximum tasks/hour, LLM type, data sources). No single party can force changes unilaterally.
5. Transparency & Security
6. • All fee transactions, token mint/burn events, and agent configurations are recorded on the blockchain, delivering auditable, tamper-proof logs.
   • The agent’s logic for tasks, data pipelines, and usage limits is open-source and verifiable, fostering trust among stakeholders.

Conclusion

By integrating bonding curves directly into each agent’s token contract and pairing it with $EXACT, Exact Agent enables partial ownership, automatic price discovery, and a sustainable fee model for computation and storage. Developers can launch new agents—complete with tasks, goals, and usage parameters—just as easily as deploying a token on-chain. Holders of the agent’s tokens gain a stake in the agent’s growth and can influence its trajectory through on-chain governance. Meanwhile, the task-based fee structure ensures a direct correlation between resource usage and payment, keeping the Exact Agent mainnet robust, secure, and profitable for both creators and community participants.