RFP: OLAS x Shutter DAO 0x36 Collaboration on Gnosis Chain

Proposal Overview Should this proposal be approved, Shutter DAO 0x36 and OLAS will issue a Request for Proposals (RFP) aimed at developers and teams interested in building an AI-agent-driven simulator for MEV and transaction ordering strategies on Gnosis Chain. This initiative will utilize the shutterized, encrypted mempool and integrate OLAS autonomous agents, focusing on an experimental, research-centric approach to explore MEV solutions and transaction ordering strategies.

As the shutterized Gnosis Chain prepares for its imminent mainnet launch, this collaboration presents an exciting opportunity to leverage OLAS’s autonomous agents and Shutter’s encrypted mempool. The project will emphasize deep research and analytics in a mainnet environment using actual funds.

This is more of a general idea and an open RFP and proposers are encouraged to be creative and proactive regards to the scope of work, execution and additional ideas/components.

Project Description The simulator will involve LLM-powered autonomous agents in a controlled mainnet setting. These agents will interact with the encrypted mempool, allowing for a robust analysis of economic and technical outcomes to refine system functionalities based on empirical data.

Example Scope of Work

1. OLAS Autonomous Agents Framework Setup

   • Modify and deploy OLAS’s existing trading agents to simulate diverse behaviors within the MEV ecosystem.

2. Creation and operation of Autonomous Agents

   • Develop a minimum viable product (MVP) to simulate normal trading behaviors alongside standard MEV and front-running strategies.
   • Operate (or ensure the operation of) the agents for a certain time period or make a proposal on how to ensure the operation.
   • Sidenote: The agent system could already be tested on the shutterized Chiado testnet (https://twitter.com/Swapr_dapp/status/1801540149429604653) and the Swapr V3 deployment which is live on top of it.

3. Research and Analytics Dashboard Development

   • Design and implement an analytics dashboard to monitor and analyze the performance of autonomous agents. This dashboard will focus on agents' P&L, MEV activities, and the effectiveness of using an encrypted mempool, aiming to draw parallels and infer potential impacts on MEV mitigation on Ethereum Layer 1.

4. MEV Mitigation Research Through Encrypted Mempool Usage

   • Conduct a study to compare the performance of agents using the encrypted mempool against those who do not. This research aims to evaluate how encrypted mempool usage influences MEV occurrences and trading strategies, with the goal of extrapolating these findings to assess potential MEV prevention on Ethereum Layer 1.

Rewards and Incentives Successful applicants will share rewards up to 30,000 USDC, 300,000 SHU (the SHU would be subject to 1 year vesting), distributed among all grant recipients based on the completion and success of the proposed projects.

Sidenote: OLAS has a native dev rewards mechanism for several 100k $ per epoch (~1 month) to developers. Any agent use case minted on the OLAS registry would qualify for these rewards.

Motivation The primary goal of this RFP is to bridge theoretical and practical knowledge gaps in transaction supply chains, particularly in the MEV and transaction ordering fields. By promoting hands-on experimental research, this collaboration aims to enhance understanding, security, and efficiency within the DeFi space.

Pre-requisites

• Expertise in blockchain development, especially within Ethereum, Gnosis Chain.
• Demonstrated experience or a keen interest in MEV and transaction ordering strategies.
• Commitment to maintaining the operational aspects of the deployed systems throughout the agreed period.

Voting Options

• YES: Approve the RFP launch and allocate the proposed rewards for project development.
• NO: Reject the proposal.
• ABSTAIN: Opt not to vote on this proposal.

License CC0: This work is dedicated to the public domain under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.