AI-Agent-Driven MEV and Transaction Ordering Simulator on Gnosis Chain| OLAS x Shutter DAO 0x36 - ISOTROPIC SOLUTIONS RFP Response

[isotropic]

Summary

Isotropic Solutions is honored to respond to Shutter DAO 0x36 and OLAS RFP to build an AI-agent-driven simulator for MEV and transaction ordering strategies on Gnosis Chain. The focus is to leverage the shutterized, encrypted mempool and integrate OLAS autonomous agents, establishing a playground for the practical exploration of MEV solutions and transaction ordering strategies.

Problem Statement

The DeFi ecosystem faces challenges related to MEV (Miner Extractable Value) and transaction ordering strategies, which can impact fairness and efficiency. Shutter DAO 0x36 and OLAS aim to create a practical exploration environment for MEV solutions using AI-driven autonomous agents and an encrypted mempool on Gnosis Chain.

Scope of Work

• Set up OLAS Autonomous Agents Framework

• Create OLAS Agents for MEV simulation

• Develop a monitoring dashboard

• Conduct encrypted mempool testing

• Implement optional enhancements (e.g.Liquidity Providing, token minting)

Expected Outcomes

1. A functional AI-agent-driven simulator for MEV and transaction ordering strategies

2. Comparative analysis of agent performance with and without encrypted mempool

3. Insights into economic and technical implications of various trading behaviors

4. Open-source codebase and comprehensive documentation

SOLUTION AND ARCHITECTURE

MODULAR SYSTEM ARCHITECTURE

Our Approach: A modular, layered and scalable architecture designed to simulate MEV and transaction ordering strategies using AI-driven agents.

This new system integrates with Gnosis Chain and leverages Shutter’s encrypted mempool.

1. Presentation Layer: It is responsible for user interaction and data visualization.

• Dashboard UI: The main interface for users to interact with the system.

• API Endpoints: External interfaces for integration with other systems or services.

• Visualization Module: Handles data visualization for analytics and monitoring.

2. Application Layer: This layer contains the core application logic and services.

• Agent Management: Handles creation, configuration, and lifecycle of AI agents.

• Strategy Execution: Executes trading and MEV strategies for the agents.

• Simulation Control: Manages the overall simulation process and parameters.

• Data Analysis Services: Processes and analyzes data from simulations and agent activities.

3. Domain Layer: This layer encapsulates the core business logic and models of the system.

• Core Business Logic: Implements the fundamental logic of MEV strategies and market dynamics.

• Agent/Strategy Models: Defines the structure and behavior of agents and their strategies.

• Simulation Rules: Sets the rules and constraints for the simulation environment.

• Game Theory Models: Implements game theory concepts for multi-agent interactions.

4. Infrastructure Layer: This handles data persistence, external integrations, and system-level operations.

• Data Persistence: Manages storage and retrieval of simulation data and results.

• Blockchain Integration: Interfaces with the Gnosis Chain for transaction processing and block data.

• Logging/Monitoring: Handles system logging and performance monitoring.

• Encrypted Mempool Adapter: Interfaces with Shutter’s encrypted mempool for private transaction handling.

• Stream Processing: Manages real-time data processing for high-performance simulations.

This layered architecture ensures separation of concerns, modularity, and scalability in the MEV simulator system.

KEY COMPONENT BREAKDOWN

1. Agent Framework

1. Manages agent lifecycle, decision-making processes, and interactions.

2. Implements reinforcement learning algorithms for agent behavior optimization.

3. Built on modified OLAS autonomous agent system.

2. Strategy Implementation Module

1. Defines and implements various trading and MEV strategies.

2. Includes normal trading, front-running, sandwich attacks, and arbitrage strategies.

3. Utilizes a strategy factory pattern for easy addition of new strategies.

3. Simulation Environment

1. Replicates Gnosis Chain testnet conditions.

2. Manages simulation clock and event scheduling.

3. Handles agent interactions and transaction processing.

4. Data Collection and Analysis Module

1. Collects performance metrics, transaction data and agent behaviors.

2. Implements data processing pipelines for real-time and batch analysis.

3. Utilizes machine learning models for pattern recognition and anomaly detection.

5. Integration Layer

1. Interfaces with Gnosis Chain and Shutter’s encrypted mempool.

2. Manages blockchain interactions including transaction submission and block processing.

3. Implements adapters for different blockchain APIs and protocols.

6. Dashboard

1. Provides real-time visualization of simulation data and agent performances.

2. Offers configurable views and interactive data exploration tools.

KEY TECHNICAL FEATURES OF COMPONENTS AND INDICATIVE CLASS DIAGRAM

KEY TECHNICAL FEATURES - DETAILS

1. Agent Intelligence

• Implements deep reinforcement learning using TensorFlow

• Utilises combination of DDPG (Deep Deterministic Policy Gradient) and PPO (Proximal Policy Optimisation) algorithms

• Incorporates replay buffer for experience replay and more stable learning

2. MEV Strategy Implementation

• Implements flexible strategy pattern for easy addition of new MEV strategies

• Utilizes game-theory concepts to model complex multi-agent interactions

• Incorporates probabilistic models to simulate realistic market conditions and agent behaviors

3. Encrypted Mempool Integration

• Implements a custom adapter for Shutter’s encrypted mempool

• Utilizes homomorphic encryption techniques to process encrypted transactions

• Implements a secure key management system for handling encrypted data

4. Scalability and Performance

• Utilizes actor model (ex. Akka) for concurrent agent simulations

• Implements efficient data structures (e.g.order book as red-black tree) for high-performance trading simulations

• Uses stream processing (e.g. Apache Flink) for real-time data analysis

5. Data Analysis and Visualization

• Implements a Lambda architecture for handling both real-time and batch data processing

• Utilizes Apache Spark for large-scale data analysis

• Implements D3.js and WebGL for high-performance, interactive data visualizations

6. Security Considerations

• Implements secure multi-party computation for privacy-preserving agent interactions

• Utilizes zero-knowledge proofs for verifiable computation in MEV strategies

• Implements robust access control and audit logging for all system component

TECH STACK

• Blockchain: Gnosis Chain, Ethereum-compatible tools

• Smart Contracts: Solidity

• Agent Development: Python, OLAS SDK

• Backend: Node.js, Express.js

• Frontend (Dashboard): React.js, D3.js for visualizations

• Database: PostgreSQL

• Testing: Truffle, Mocha, Chai

• DevOps: Docker, Jenkins, GitHub Actions

Note: Solution approach and choice of tech stack subjected to detailed understanding of eco-system / requirements.

EXECUTION APPROACH

Our execution approach is a hybridized model build for rapid delivery with an underlying foundation prepared for scale.

Our approach, summarized here, is the culmination of 2 decades of hardening and high-level performance while working at the world’s largest company and subsequently providing services for international banks, multi-billion dollar manufacturing companies, and some of the world’s largest brands including.

This approach has demonstrated the ability to meet immediate objectives while laying a foundation for future growth. All without sacrificing security or quality.

EFFORT & SCHEDULE

KEY RISK, ISSUES AND DEPENDENCIES

1. Encrypted Mempool Integration

• Risk: Challenges in integrating with Shutter’s encrypted mempool

• Support Needed: Technical documentation and potential direct support from Shutter team

2. MEV Strategy Complexity

• Risk: Difficulty in simulating complex MEV strategies accurately

• Support Needed: Access to historical MEV data or expertise from the community

3. Scalability of Simulation

• Risk: Performance issues when scaling up the number of agents

• Support Needed: Guidance on Gnosis Chain’s performance characteristics and best practices

4. Regulatory Considerations

• Risk: Potential regulatory issues related to MEV simulation

• Support Needed: Legal guidance from the Shutter DAO or OLAS community

5. Data Privacy and Security

• Risk: Ensuring the privacy and security of simulated trading data

• Support Needed: Best practices for data handling in MEV contexts

TIMELINE OPTIONS

We have prepared two plans in this RFP Response to accommodate preferences from the ShutterDAO community.

Isotropic Solution anticipates pursuing PLAN 2 due to it’s rapid delivery of a usable MVP, whereas PLAN 1 affords more up-front time to lay out a robust foundation for long-term growth.

PLAN 1 (Option)

Phase 1 (IN SCOPE): Foundation and Basic Agent Implementation (Weeks 1-5)

Outcome: Functional MEV simulation environment with basic AI-driven agents on Gnosis Chain testnet.

Business Value:

• Early proof-of-concept

• Tangible demonstration for stakeholders

• Foundation for further development

Phase 2 (IN SCOPE): MEV Strategy Implementation and Dashboard (Weeks 6-9)

Outcome: Sophisticated simulation with complex MEV strategies and monitoring dashboard.

Business Value:

• Observe various MEV strategies in action

• Early insights into market dynamics

• Visual tool for stakeholder engagement

Phase 3 (optional addition): Encrypted Mempool and Advanced Features (Weeks 10-12)

Outcome: Full MEV simulation with encrypted mempool integration and advanced analysis capabilities.

Business Value:

• Unique insights into privacy-preserving MEV

• Advanced pattern recognition and multi-agent dynamics

• Potential for academic/industry partnerships

Phase 4 (optional addition): Optimization (Weeks 13-15)

Outcome: Production-ready, large-scale MEV simulation platform with comprehensive documentation and analysis.

Business Value:

• Robust tool for ongoing MEV research

• Actionable insights for DeFi protocol design

• Positioning as leaders in MEV research

Overall Project Value

1. Cutting-edge MEV research platform

2. Unique insights into MEV in privacy-preserving environments

3. Data-driven basis for MEV mitigation strategies

4. Potential for academic publications and industry partnerships

5. Positioning at the forefront of DeFi innovation

PLAN 2 (Recommended Option)

Phase 1 (IN SCOPE): MVP with Basic MEV Simulation (Weeks 1-8)

Deliverables:

1. Functional MEV simulation environment on Gnosis Chain testnet

2. Basic AI-driven agents with simple MEV strategies (e.g. front-running)

3. Simple dashboard for visualizing agent actions and MEV outcomes

4. Integration with a simplified version of encrypted mempool

Outcome: A Minimum Viable Product (MVP) that demonstrates the core concept of MEV simulation, including basic encrypted transaction handling.

Business Value:

• Early, functional proof-of-concept of the entire system

• Ability to run simple MEV simulations and visualize results

• Initial insights into MEV dynamics with and without basic transaction privacy

Phase 2 (optional addition): Enhanced Strategies and Analysis (Weeks 8-10)

Deliverables:

1. Expanded set of MEV strategies (e.g., sandwich attacks, arbitrage)

2. Improved agent intelligence using reinforcement learning

3. Enhanced dashboard with more detailed analytics

4. Basic comparative analysis framework for encrypted vs. non-encrypted transactions

Outcome: A more sophisticated simulation environment with richer MEV strategies and initial comparative analysis capabilities.

Business Value:

• Deeper insights into complex MEV behaviors

• Ability to compare strategy effectiveness in different scenarios

• More robust data for initial research findings or publications

Phase 3 (optional addition): Advanced Features and Scalability (Weeks 10-13)

Deliverables:

1. Full integration with Shutter’s encrypted mempool

2. Implementation of advanced game theory models

3. Scalability improvements for large-scale simulations

4. Advanced data analysis with machine learning for pattern recognition

Outcome: A scalable, feature-rich MEV simulation platform capable of handling complex scenarios and large-scale simulations.

Business Value:

• Comprehensive insights into MEV in privacy-preserving environments

• Ability to run and analyze large-scale, realistic simulations

• Potential for groundbreaking research findings

Phase 4 (optional addition): Optimization (Weeks 13-16)

Deliverables:

1. Performance optimizations across the entire system

2. Comprehensive testing suite and bug fixes3. Complete documentation (technical, user guides, API)

3. Final comparative analysis report and research paper draft

Outcome: A production-ready, well-documented MEV simulation platform with actionable insights and research outputs.

Business Value:

• Robust tool for ongoing MEV research and strategy development

• Publishable research findings and potential for academic/industry partnerships

• Positioning as leaders in MEV and privacy-preserving DeFi research

TEAM STRUCTURE

Our team structure includes 2 AI Agent developers fully trained by OLAS’s team via their intense, full-time, month-long formal training program.

We then add additional support including experienced architect oversight, day-to-day management and additional developers with added skillsets for various project deliverables and quality validation.

Executive Sponsor: Our Executive Sponsor is Isotropic’s Global CTO, having extensive experience in financial technologies and concepts. He’s additionally supported by the rest of the Isotropic Solutions team. Additional team experience is available upon request.

Our team currently works with international banks, multi-billion dollar manufacturers, agriculture companies and telecommunications clients. We are delivering AI Agent ecosystems, ML-driven business solutions, cloud transformation and automation and cybersecurity solutions.

TEAM:

1. System Architect (0.5x)

◦ Skills: Blockchain , AI / Autonomous Agent design, Machine Learning , Python and System Architecture

2. Engineering Manager (1x)

◦ Skills: Agile methodologies, blockchain project management, Machine Learning

3. Blockchain Developers (2x)

◦ Skills: Solidity, Gnosis Chain, MEV strategies

4. OLAS Agent Specialists (2x)

◦ Skills: OLAS SDK, autonomous agent development, Python, Machine Learning

5. Full-stack Developer (1x)

◦ Skills: Node.js, React.js, database management, UI

6. Data Analysts / QA Engineer (1x)

◦ Skills: Blockchain testing, automated testing frameworks, Data analysis, visualization

PROPOSAL SUMMARY

ABOUT ISOTROPIC SOLUTIONS

About Isotropic Solutions

Delivering Tomorrow’s Solutions, Today.

Isotropic is an AI, Cloud Automation and Cybersecurity Company. It was founded in 2022 by partners who have worked together for 20 years, delivering enterprise grade solutions to the world’s largest and most successful companies. After exiting their previous company and shifting full-time into finance and blockchain in 2021, they founded Isotropic, assembled another talented team, and now work with high-performance teams around the globe.

Website: isotrp.com