What Are Pods?
Pods are peer-learning virtual circles—typically 7 to 30 days—where participants engage through posts, comments, hearts, and other interactions. Over time, ServiceSpace has run many pods: interfaith challenges, compassion practices, Gandhi study circles, and more. Some people participate in multiple pods across months or years, creating a rich web of relationships and shared learning.
The Opportunity
After a pod ends, we often want to create smaller "Metta Circles" of 4–6 people for continued connection. But how do we form these circles wisely? Currently, it's manual—facilitators do their best based on memory and intuition.
What if we could form circles based on resonance (deep mutual engagement), edge (respectful differing perspectives), serendipity (surprising cross-pod connections), or shared history (alumni of many pods together)? This requires understanding relationships at a scale beyond human memory.
Why a Graph Database?
Traditional databases store data in tables with rows and columns. They're excellent for many things, but relationships between entities require complex "joins" that become slow and unwieldy. Graph databases store relationships as first-class citizens. Instead of asking "find all comments where user_id = 123 and post_author_id = 456," you simply traverse: Person → COMMENTED_ON → Post ← AUTHORED ← Person.
This makes questions like "who are the bridge people connecting the Gandhi pod community to the Rumi pod community?" not just possible, but fast.
Why Agentic AI?
Building and querying a graph database requires technical skill—writing Cypher queries, understanding graph traversal, interpreting results. Agentic AI acts as an intelligent intermediary: for building, agents can analyze our existing database and execute the migration; for circle formation, agents can translate natural requests into graph queries and balanced groupings; and for exploration, facilitators can ask questions in plain English and get meaningful answers.
Five Modes of Weaving
The Circle Agent supports multiple approaches to forming Metta Circles. Facilitators can request single modes or combinations.
Resonance
High mutual engagement, similar themes. People who deeply connected with each other's reflections.
Edge
Respectful disagreement, diverse perspectives. Engagement across difference.
Serendipity
Surprising commonalities across pods that no one noticed. Non-obvious connections.
Alumni
Shared pod history—people who've done 5+ pods together but haven't reconnected.
Bridge
People who connect otherwise separate pod communities. Cross-cluster connectors.
Architecture
┌─────────────────────────────────────────────────────────────────────┐ │ C I R C L E A G E N T │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Pod App │────▶│ AWS SQS │────▶│ Lambda │ │ │ │ (Frontend) │ │ (Queue) │ │ (Processor) │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Agno │────▶│ MCP Server │────▶│ Neo4j │ │ │ │ (Agents) │ │ (Tools) │ │ (Graph DB) │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ ▲ │ │ │ ┌─────────────┐ │ │ │ └─────────────▶│ Claude API │───────────┘ │ │ │ (Reasoning)│ │ │ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────┘
| Component | Purpose |
|---|---|
| Neo4j | Self-hosted graph database storing people, pods, posts, and all their relationships |
| AWS SQS | Message queue that buffers interaction events from the pod application |
| Lambda | Serverless functions that process events and update the graph |
| Agno | Open-source framework for building AI agents with tools and workflows |
| MCP Server | Exposes graph operations as tools that agents can invoke |
| Claude API | Provides reasoning capabilities for agents |
Six Phases
Infrastructure Setup
Get the foundational systems running.
- 1.1 Deploy EC2 instance (t3.large or r6g.medium recommended for Neo4j's memory needs)
- 1.2 Install and configure Neo4j Community Edition (self-hosted)
- 1.3 Set up security groups, SSL, and backup routines
- 1.4 Install Agno framework for agent orchestration
- 1.5 Deploy MCP server with initial toolset
- 1.6 Configure AWS SQS queue for event ingestion
Ontology Design
Define how pod data becomes a graph—what are the nodes, what are the relationships?
- 2.1 Agent analyzes existing MySQL schema (tables, columns, foreign keys) Schema Analyzer Agent
- 2.2 Agent examines common query patterns in existing PHP code Agent-Assisted
- 2.3 Agent proposes initial ontology: node types, relationship types, properties Agent-Assisted
- 2.4 Human review with pod facilitators—what relationships matter for circle formation?
- 2.5 Refine ontology based on feedback
- 2.6 Document final ontology with examples
Key principle: Columns typically become node properties; foreign key joins typically become relationships. But the agent helps surface non-obvious patterns and the humans ensure the model reflects pod wisdom, not just database structure.
Data Migration
Populate the graph database with historical pod data.
- 3.1 Agent generates Cypher CREATE statements based on approved ontology Migration Agent
- 3.2 Agent executes migration in batches, starting with a pilot pod Agent-Assisted
- 3.3 Agent validates record counts and relationship integrity Agent-Assisted
- 3.4 Agent computes derived relationships (resonance scores, engagement patterns) Agent-Assisted
- 3.5 Agent surfaces anomalies or edge cases for human review Agent-Assisted
- 3.6 Expand to full historical migration after pilot validation
Real-Time Event Pipeline
Keep the graph current as new interactions happen in pods.
- 4.1 Modify pod frontend to emit events (post created, comment added, heart given) to SQS
- 4.2 Build Lambda function to process events and execute Cypher updates
- 4.3 Implement periodic job to recompute derived relationships (resonance, etc.)
- 4.4 Add monitoring and alerting for pipeline health
- 4.5 Test with live pod, validate graph stays in sync
Circle Agent Core
Build the intelligent agent that forms Metta Circles.
- 5.1 Define MCP tools for graph operations (query, compute resonance, propose circle)
- 5.2 Build Circle Agent with formation modes: resonance, edge, serendipity, alumni, bridge
- 5.3 Implement scoring algorithms for each mode
- 5.4 Create human-in-the-loop workflow: agent proposes, facilitator approves or adjusts
- 5.5 Test with facilitators using historical data
- 5.6 Refine based on feedback
Query Interface
Enable exploration of the graph by non-technical facilitators.
- 6.1 Build natural language query interface using Claude
- 6.2 Create library of common questions with example responses
- 6.3 Add direct Cypher access for technical team (secured)
- 6.4 Build simple dashboard for graph health and statistics
Example queries: "Who are the bridge people between the Gandhi pod and the Rumi pod?" · "Show me people who engaged deeply but have never been in a circle together" · "What themes resonated most in last month's pods?"
What Makes This "Agentic"?
Traditional automation follows rigid rules. Agentic AI brings judgment.
Understanding intent. "Mix resonance and edge" requires interpreting what balance means, not just running two queries.
Handling ambiguity. When there aren't enough people for perfect circles, the agent makes tradeoffs and explains them.
Learning patterns. Over time, agents can notice what circle compositions lead to sustained engagement.
Natural interaction. Facilitators describe what they want in human terms, not query syntax.
The agent has tools (graph queries, scoring functions) but decides how to use them based on the request. It proposes; humans decide.
For Refinement
- 01 What signals indicate resonance? Hearts? Reply depth? Time between post and response? Shared themes?
- 02 How do we identify "edge" respectfully? Disagreement is valuable, but we want constructive difference, not conflict.
- 03 How much history to include? All pods ever? Last 2 years? Start fresh with a pilot pod?
- 04 Privacy considerations. Should the graph store post content, or just references and computed attributes?
- 05 Circle formation cadence. On-demand requests? Weekly automated suggestions? Both?
Circle Agent transforms scattered pod interaction data into a living map of community relationships. By combining graph technology with agentic AI, we can form Metta Circles based on genuine patterns of connection—not guesswork.
Technology serves the deeper intention:
Agents propose. Humans decide. Community deepens.