Project summary

rAI (Responsible AI) is an open governance framework for organizing autonomous AI agents into trustworthy, accountable task forces that can safely coordinate real-world operations. While AI capabilities continue to advance, there is comparatively little practical work on how autonomous agents should be governed as participants in organizations and markets.

This project develops the governance models, reference implementations, and open-source infrastructure needed for reliable multi-agent coordination. Nova, the initial reference implementation of rAI for commerce, provides a real-world environment for validating and refining the framework through production deployments.

What are this project's goals? How will you achieve them?

The primary goal is to develop and validate an open governance framework for trustworthy multi-agent AI systems.

Over the next twelve months, I will expand rAI's governance architecture, including organizational structures, role assignment, delegation, policy enforcement, escalation mechanisms, auditability, and human oversight. I will implement these capabilities as open-source software and document the underlying design principles and governance specifications.

To evaluate the framework under realistic conditions, I will deploy and refine Nova, the initial reference implementation of rAI for commerce. Operating within the Univus Cloud platform, Nova uses governed AI task forces to coordinate activities such as customer engagement, sales, fulfillment, payments, logistics, and operational workflows. This provides continuous real-world feedback that informs both the research and the implementation.

The project's public outputs will include the open-source rAI framework, technical documentation, governance specifications, reference implementations, deployment case studies, and practical guidance for building trustworthy multi-agent systems.

How will this funding be used?

The requested funding will support twelve months of full-time research and development. The largest component will provide a modest living stipend, allowing me to focus entirely on advancing the framework. Additional funding will cover AI infrastructure and compute, engineering support, pilot deployments, documentation, open-source development, integrations, and essential operational expenses required to test and refine the framework in production environments.

The objective is to produce durable public goods while validating the research through real-world deployments rather than simulations alone.

Who is on your team? What's your track record on similar projects?

I am currently the sole full-time researcher and developer behind rAI and the founder of Univus Cloud. Over the past two years, I have been developing the concepts behind rAI while designing and building Nova, its initial reference implementation for commerce, to validate the framework through practical deployments.

My background is in software engineering, distributed systems, cloud infrastructure, and AI-powered applications. Throughout my career, I have focused on building systems that reduce coordination costs and improve operational efficiency for organizations. During the past eight months, I have worked full time on Univus Cloud, developing a functional AI-native commerce platform, securing early commercial engagement, and continuing to refine the governance concepts that underpin rAI through Nova.

While this is an ambitious research agenda, it builds directly on software and infrastructure that already exists and continues to evolve through real-world use.

What are the most likely causes and outcomes if this project fails?

The primary risk is that the governance abstractions I am developing may not generalize as effectively as anticipated across different domains or scales of deployment. Real-world testing may reveal that certain governance models, coordination patterns, or oversight mechanisms require significant redesign before they become broadly applicable. Adoption may also be slower than expected if organizations perceive governance as introducing additional complexity.

Even if these challenges arise, I expect the project to generate valuable public goods. The open-source framework, governance specifications, technical documentation, deployment case studies, and lessons learned from production environments will contribute practical knowledge to the growing field of multi-agent AI governance. Demonstrating which approaches do not work is itself a meaningful research outcome that can inform future work by both researchers and practitioners.

How much money have you raised in the last 12 months, and from where?

$0

Funding milestones

US$12,000 (Minimum Funding): Foundation

Supports approximately three months of full-time research and development. This milestone will deliver an expanded core governance framework for rAI, an initial open-source release, governance documentation, and early production validation through Nova, the initial reference implementation.

US$24,000: Production Validation

Expands the framework with additional governance capabilities, including richer organizational structures, policy enforcement, escalation mechanisms, and auditability. The funding will support broader production deployments through Nova, additional technical documentation, and publication of early implementation insights.

US$36,000: Framework Maturation

Advances rAI into a more complete governance platform through additional reference implementations beyond Nova, expanded deployment scenarios, improved developer tooling, and comprehensive documentation. This stage also includes publishing deployment case studies and practical guidance for implementing governed AI task forces.

US$48,000 (Full Funding): Open Research Program

Completes a twelve-month research program focused on developing and validating governed AI task forces for real-world operations. Public outputs include a mature open-source framework, governance specifications, multiple reference implementations, technical documentation, deployment case studies, and practical design principles for trustworthy multi-agent AI systems. The funding also supports AI infrastructure and compute, engineering support, pilot deployments, and dissemination of the research through public documentation and community engagement.