# Gaia AI & Block Science Meeting - KOI Implementation Discussion

**Participants:**
- Gaia AI: Shawn Anderson, Darren Zal
- Block Science: Peter, Jamshid, Luke, Michael Zargham
- Mentioned: Gregory Landua

## Opening and Introductions

The meeting opened with participants gradually joining the call, while the Block Science team mentioned that Fathom and Otter note-taking tools were running to capture the discussion. After initial greetings, the group decided to wait a few minutes for others to join before formally beginning.

As they waited, one participant from Block Science shared that he was working toward starting his master's thesis, though the work hadn't quite begun yet. His focus was converging on multidisciplinary design optimization (MDO), a topic he had previously presented to Block Science. He explained that MDO serves as a coordination tool in systems design contexts, where teams of engineers work independently on subsystems of larger projects. Each team runs intensive disciplinary analysis software for their particular subsystem and is responsible for optimizing their piece of the puzzle. However, since these subsystems must ultimately integrate into a cohesive whole, local design decisions inevitably affect other teams' work. This creates a complex optimization problem around information sharing and finding solutions that work for both individual teams and the overall system design.

## The Gaia AI and Regen Network Collaboration

Once the meeting formally began, the Gaia AI team introduced their work creating AI agents focused on ecological and regeneration applications. They explained their partnership with Regen Network to develop Regen AI, which has two primary objectives: facilitating 100,000 user interactions with AI agents and indexing 15,000 knowledge objects to make them accessible to these agents. The development team brings extensive backgrounds in software engineering, computer science, data engineering, data science, and machine learning to the project.

The Gaia AI developers, both based on Vancouver Island, expanded on their work creating an intelligent, agentic system where knowledge management forms a critical component. The system encompasses internal knowledge management for conversations, documents, and Notion content, as well as a public component where agents engage and post content on social media platforms. They're also working on the Cognizant Initiative, which aims to create a Regen commons - essentially a federation or network of organizations that share branding, resources, and importantly, knowledge. This initiative raises important considerations around permissions, privacy, and controlled information sharing between organizations.

The team has made significant progress indexing content from various sources including Twitter, websites, Notion, Discord, and Telegram. They've begun implementing KOI (Knowledge Object Interface) infrastructure using Regen Network's existing RID (Resource Identifier) naming conventions, which Regen had already established in a GitHub repository containing their governance and naming standards.

## Defining Knowledge in Complex Systems

The discussion took a philosophical turn when a Block Science member asked for a working definition of knowledge, noting the importance of establishing common ground when discussing knowledge systems. This sparked a rich exchange about the nature of knowledge itself. The Gaia AI team offered their perspective on the data-information-knowledge-wisdom pyramid, viewing knowledge as synthesized information that sits between raw data and wisdom. They further distinguished between embodied knowledge - what we carry in our bodies and brains that we can dialogue about - and recorded knowledge, which encompasses paper documents and digital records.

The Block Science perspective contributed a view that knowledge functions more as an organizational property - an organization's internal model of what's happening both within itself and in its environment. Knowledge is more than just facts flowing in and decisions flowing out; it's the complex processing that happens in between. This led to a discussion of knowledge as process, as the flow between agents in an organization, which aligns with Block Science's perspective of viewing everything as dynamical systems where no element exists in isolation but rather as part of ongoing processes.

## Technical Implementation and Demonstrations

The Gaia AI team demonstrated their current implementation using the ElizaOS agent framework, which provides interfaces for running multiple agents. The team is developing four primary agents for the Regen ecosystem. The Advocate agent serves as a representative for ecosystem service credits, payment for ecosystem services, and various ecological credits deployed on the Regen chain. The Governor agent specializes in Regen Network's governance protocols, policies, practices, and history. The Narrator acts as Regen's storyteller, building excitement and attracting attention to the network's mission. Finally, the Voice of Nature represents natural ecosystems, serving as Earth's voice in the digital realm.

The technical architecture employs a RAG (Retrieval-Augmented Generation) system where character files contain agent-specific information and system prompts. The system maintains conversation history and creates memories from interactions, incorporating these into future responses. The agents connect to various platforms including Discord, Telegram, and Farcaster, though the team showed the out-of-the-box interface during the demonstration.

The demonstration also included their KOI interface implementation, which currently manages approximately 13,000 indexed documents, predominantly tweets but also including content from Notion, Medium, GitHub, GitLab, and various websites. The system tracks which agents have access to specific content and maintains source attribution. They've developed a JSON manifest system that captures metadata and content references, assigning RIDs according to Regen Network's specifications.

The team is exploring integration with RDF, SPARQL, and OWL technologies, which aligns well with Regen Network's existing data module that uses RDF and linked data. They're also investigating logical inferencing capabilities, which could add sophisticated reasoning to the system. However, they acknowledge that their current implementation isn't yet a full KOI v3 node, as it lacks federation protocols and network communication capabilities that would enable multiple nodes to communicate with each other.

## The Reality of Building Distributed Knowledge Systems

A Block Science team member provided what he described as both validation and a reality check for the team's ambitions. Drawing from Block Science's years of experience wrestling with similar challenges, he emphasized that the middle layer of these systems - between data ingestion and user experience - presents the most significant challenges. While gathering data and implementing AI might seem straightforward based on current discourse, the reality involves complex challenges around entity resolution, event detection, temporal relevance, and making implicit organizational knowledge explicit.

The discussion highlighted that RAG systems particularly struggle with temporal aspects - determining what information is current versus outdated isn't something that comes for free, especially when dealing with fragmented systems where the same information might exist in multiple places with different update cycles. An old Medium article might contain information that's been superseded by a newer document elsewhere, but encoding these relationships requires deliberate design choices that can't be inferred directly from the data.

The Block Science team strongly advocated for starting small and building incrementally. They emphasized that no network was ever built without first creating a node, then a second node, then an edge between them. While this approach might seem unsexy compared to ambitious network-building goals, it's the realistic path to creating functional knowledge networks. Teams should carve out manageable subsets of their ultimate vision, create working closed-loop data products for these smaller scopes, and then iterate and expand.

As a practical exercise, it was suggested that the Gaia AI developers begin by making their personal knowledge management practices interoperable. Each could set up their system as a model-view-controller architecture where they maintain full control over their own knowledge management, expose selected views through KOI sensors to each other, and work through the inevitable challenges of entity resolution and establishing common references. This exercise would provide hands-on experience with the fundamental challenges of distributed knowledge systems before attempting to scale to organizational or network levels.

## Environmental Applications and Bioregional Knowledge Commons

The conversation expanded to discuss applications in bioregional and environmental governance, with the assertion that governance cannot exist without knowledge management - they're fundamentally inseparable. The opportunity was framed as one of digital transformation for existing commons-based governance initiatives rather than trying to build entirely new systems from scratch.

The key is to start with a particular community, map their sites of knowledge production and how they relate to each other, understand the decisions and rules that community makes and enforces, and then digitalize these processes in ways that make monitoring and enforcement easier. Only after successfully digitalizing single communities should teams attempt to expand to knowledge sharing between organizations.

The discussion introduced the concept of cosmo-local knowledge commons governance as a prerequisite for cosmo-local bioregional commons governance. The knowledge infrastructure must come first - it cannot be skipped. This involves a fundamental tradeoff between interoperability and representativeness. Local communities managing specific resources possess vast amounts of knowledge, but only a small subset can be digitalized, and even less can be made interoperable with other systems while maintaining its contextual meaning.

The recommendation was to start with sites that naturally overlap - communities that share members, resources, or physical constraints. For environmental applications, this might mean finding two distinct but overlapping governance sites within a bioregion and first raising their level of digitalization while respecting their existing governance structures, then providing affordances for information sharing that improves coordination while respecting boundaries on what they're willing and able to share.

The Gaia AI team resonated with this approach, mentioning the Salish Sea ecosystem as a perfect example - it's been artificially divided between the United States and Canada, creating a situation where researchers and communities on either side of the border struggle to coordinate their understanding and management of what is fundamentally a single ecosystem.

## Technical Architecture and Development Pathways

The Block Science team outlined the layered architecture of the KOI project. At the base lies the protocol level, primarily managed by one team member. Above that sit reference implementations, currently in Python with TypeScript implementations planned. The next layers include node implementations and network implementations, with different team members focusing on different levels.

After over two years of development, many components remain in active research phases. Recent progress includes a protocol upgrade enabling secure identity between nodes using cryptographic signatures and verification - a crucial capability that had been blocking more complex features around information sharing between organizations and knowledge partitioning within systems. While this upgrade is now in beta testing, the team acknowledged they're still several steps away from something that external developers unfamiliar with Block Science could easily adopt and build upon.

The most accessible contribution level currently sits at the node level, as the underlying protocol and reference implementations need more stability. The team envisions that KOI will become most useful as developers create KOI-compatible capabilities tailored to their specific needs. They're actively seeking feedback on implementations and welcome development of needed widgets and tools that could benefit the broader ecosystem.

## Concluding Insights

The meeting concluded with a shared recognition that building distributed knowledge systems requires a timeline measured in years rather than months. The gap between popular AI discourse and practical implementation reality is vast. Success requires starting with simple, concrete implementations and building systematically rather than attempting to leap directly to complex network solutions.

The discussion reinforced that while the technical challenges of data ingestion and user interface design are significant, the truly hard problems lie in the middle layer - in creating systems that can meaningfully process, relate, and make sense of distributed knowledge while respecting the autonomy and boundaries of participating entities. This work is essential for enabling the kind of coordinated environmental governance that many participants envision, but it requires patience, iterative development, and a willingness to start small and build gradually toward larger ambitions.