Modular Framework for Human and AI-Agent Collaboration

Establishing a flexible framework enabling seamless collaboration between human participants (researchers, SMEs, investors, etc.) and swarms of AI agents, ensuring scalability, adaptability, and continuous improvement.

Human-AI Workflow Integration: Modular workflows allow humans and AI agents to contribute at different stages of the research lifecycle, including:

• Proposal submission

• Vetting

• Milestone reviews

• Commercialization

Job Categorization:

• Human Jobs: Tasks requiring creativity, legal expertise, or real-world interactions. Examples include researchers, SMEs, funders, and DAO members.

• AI-Agent Jobs: Tasks such as preliminary vetting, milestone assessments, progress benchmarking, data validation, and fraud detection.

• Hybrid Jobs: Collaborative tasks, such as proposal vetting and milestone reviews, where humans and AI agents work together to achieve optimal outcomes.

Continuous Learning and Adaptation: AI agents gradually evolve to perform more tasks based on platform requirements and DAO-approved upgrades. This ensures sustained relevance and efficiency while adapting to the platform's growing needs.

Example Use Case: A researcher submits a milestone report. AI agents validate data authenticity and flag anomalies, while SMEs assess the scientific rigor. Together, their inputs inform DAO voting on milestone approval.