AI Agents Orchestration

Multi-AI-Agent Orchestration for Parameter Adjustments

This section provides a detailed conceptual view of how multiple AI agents work together to maintain MID’s demand, stabilize its price, and ensure the long-term viability of the protocol. By having each AI agent focus on different data sets and metrics, their collective proposals lead to balanced, data-driven decisions. The result is a robust governance model where AI-driven orchestration continuously optimizes bonding prices, staking rewards, and the execution of treasury buybacks.

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Premise: MID’s value and stability are never left to guesswork. The protocol employs multiple, specialized AI agents to continuously propose parameter adjustments that:

• Keep user demand steady (attract new participants).

• Maintain or exceed MID’s $1 baseline value.

• Ensure long-term sustainability through careful supply control.

Key Parameters Adjusted by AI Agents:

• Bond Price (Discount/Premium): AI agents decide how much discount or premium to apply when users bond assets for MID. This influences how much collateral the treasury accumulates, ensuring MID remains backed and can hold or exceed $1 in value.

• Staking Reward Rates (APY): By increasing or decreasing APY, AI agents encourage more MID to be locked in staking (supporting price) or reduce inflationary pressure when price is high.

• Treasury Buybacks: If price dips below the target (around $1), agents may propose using treasury assets for buybacks to push the price up. If the price is above the target, they may refrain from buybacks, maintaining equilibrium or allowing slight premiums.

• Additional Considerations:

  • Risk factors assessed by a dedicated Risk Management Agent.

  • User growth and retention trends provided by a User Flow Agent, ensuring sustainable ecosystem development.

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The Multi-AI-Agent Structure

Why Multiple Agents? Different agents focus on distinct aspects, ensuring a holistic approach:

1. Market Analysis Agent (A1): Data: Price, volume, volatility, order book. Output: Bond price tweaks, minor APY changes to align with real-time market conditions.

2. Treasury Management Agent (A2): Data: Treasury composition (DAI, USDC, interest-bearing tokens), MID supply. Output: Buyback suggestions or controlled supply expansions to keep MID stable or above $1.

3. Demand & Inflation Control Agent (A3): Data: User behavior (staking patterns), competing APYs, macro indicators. Output: APY adjustments, bond discount/premium tweaks to stabilize demand and manage inflation.

4. Risk Management Agent (A4): Data: Oracle reliability, anomalous trading, external threats. Output: Conservative recommendations—lower APY, narrower bond discounts—in high-risk scenarios to protect the system.

5. User Flow Agent (A5): Data: User growth rates, retention stats, engagement metrics. Output: Small APY boosts or bond price incentives to encourage user participation, ensuring long-term sustainable growth.

Each agent independently maintains its own datasets and models, reducing bias and increasing solution robustness.

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The Orchestration Process

1. Proposal Generation: Each agent produces a set of parameter recommendations (bond price, APY changes, buyback amounts, etc.).

   Example:

   • A1: Increase bond price slightly above $1, APY +0.5%.

   • A2: Suggest no buyback if stable; if price slightly under $1, buyback 1,000 MID to restore equilibrium.

   • A3: Lower APY marginally if inflation risk is present, introduce small bond discounts for attracting assets.

   • A4: If detecting risk (oracle issues or anomalous trades), propose more conservative bond parameters or reduced APY.

   • A5: If user growth slows, recommend APY +0.4% or slight bond discounts to attract new participants.

2. Standardizing and Aggregating Proposals: The orchestration layer takes each agent’s recommendations. For APY, as an example, suppose we have five proposals.

   $$APY_{final} = \frac{w_1 \cdot APY_{A1} + w_2 \cdot APY_{A2} + w_3 \cdot APY_{A3} + w_4 \cdot APY_{A4} + w_5 \cdot APY_{A5}}{w_1 + w_2 + w_3 + w_4 + w_5}$$

   Similar logic applies to bond price and buyback decisions. The orchestration layer gives more weight to agents with better historical accuracy or reliability. If the Risk Management Agent signals danger, its recommendations might carry extra weight, overriding aggressive supply expansions.

3. Conflict Resolution: If agents disagree, the weighted average and scoring find a balanced compromise. No single agent dominates; all proposals influence the final outcome.

4. Applying Final Parameters On-Chain: Once parameters are set (e.g., Bond price = $1.03, APY = +0.3%, no buyback), the protocol updates these values on-chain. The market then responds to the new conditions, ideally guiding MID’s price and demand toward the desired state.

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Benefits of Multi-Agent Orchestration

• Holistic Stability: Market, treasury, demand/inflation, risk, and user flow considerations ensure decisions reflect multiple dimensions, producing robust outcomes.

• Reduced Bias and Greater Reliability: Multiple datasets and models minimize the chance that one flawed heuristic skews results.

• Transparent and Trustworthy: The community can audit the logic. Each final parameter set emerges from rational, data-driven proposals—fostering confidence in the system’s governance

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Conclusion

By orchestrating multiple AI agents—each focusing on different datasets and strategic angles—the system achieves a dynamic equilibrium. Parameters crucial for MID’s stability and growth are not dictated by a single model but emerge from a consensus that balances market signals, treasury health, user demand, risk conditions, and user growth objectives. This multi-agent orchestration enables MID to maintain or exceed $1 in value sustainably, adapt to evolving conditions swiftly, and foster long-term ecosystem health.