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FOLLOW UP TO PERPLEXITY 260120

IAIP Research
260120-mission-260118075628-cont

FOLLOW UP TO PERPLEXITY 260120

Previous Inquiry: https://www.perplexity.ai/search/260120-mission-260118075628-co-FchMfUwPSwC7ZaqWrv0BHw
Previous Result: 260120-MISSION-260118075628-cont--20c06417-422d-49.md
Status: Initial survey complete across 4 domains
Next Step: Deep dive into Domain 3 (Technical Implementation)

SUMMARY OF INITIAL FINDINGS

Key Discoveries

  1. Indigenous Protocol and AI (Lewis et al. 2020) - Bridge between Wilson paradigm and AI design
  2. Abundant Intelligences / Two-Eyed AI - Validates multi-epistemology AI approach
  3. Rieger et al. Debwewin Journey - Tightest template for digital talking circle + Medicine Wheel + OCAP
  4. Trauma-Informed Care in AI - NSF TIC-chatbot project gives formal framing
  5. LangGraph + CrewAI pattern - LangGraph for ceremony spiral, CrewAI for Six Powers roles

Gaps Confirmed (Original Work Territory)

  • No Erikson + trauma + AI stage-by-stage healing implementations
  • No Dramatica in AI peer-reviewed implementations
  • No structural tension in software/AI literature
  • No systems with ceremony logs, seasons, relational obligations as first-class constraints

NEXT INQUIRY: DOMAIN 3 DEEP DIVE

Focus Area

Technical Implementation Patterns for Family Circle Recovery

We need concrete architecture specifications, code patterns, and integration recipes.

PROMPT FOR PERPLEXITY (COPY THIS)

```markdown

FOLLOW-UP INQUIRY: Technical Implementation Deep Dive

Context

This is a follow-up to our initial survey (session 260120). You identified that:

  • LangGraph is ideal for Medicine Wheel / Ceremony Spiral workflows
  • CrewAI-style roles work for Six Powers agents
  • Vector DBs need two-step retrieval with governance service post-filter
  • OCAP® compliance requires community-controlled infrastructure

We are building Family Circle Recovery - an AI-powered talking circle platform.

What We Need Now

1. LangGraph Architecture for Medicine Wheel

Request: Detailed architecture pattern for implementing a four-direction Medicine Wheel workflow in LangGraph.

Specific questions:

  • How to model East → South → West → North as graph nodes with cyclical return?
  • How to integrate structural tension (current reality vs desired outcome) as state?
  • How to implement "seasonal gates" that restrict access based on current season?
  • Example code snippets or pseudocode for the graph structure

Desired output: Architecture diagram (textual), node definitions, edge conditions, state schema

2. CrewAI Six Powers Implementation

Request: Pattern for implementing six distinct agent roles (Life, Love, Intelligence, Soul, Principle, Truth) that can participate in a healing conversation.

Specific questions:

  • How to define agent personas that embody each Power's characteristics?
  • How to coordinate agents without overwhelming the user (one voice at a time)?
  • How to integrate human-in-the-loop for Elder oversight?
  • How to implement "stepping back" when a Power recognizes it's not the right voice for this moment?

Desired output: Agent definition templates, coordination patterns, example prompts

3. Vector Store Governance Layer for Sacred Content

Request: Implementation pattern for Upstash vector store with ceremonial access control.

Our metadata requirements:

  • season: winter | spring | summer | fall
  • sacred_level: public | community_only | restricted | sacred_private
  • relation_type: ancestor | human | land | spirit | future | knowledge
  • circle_id: Specific talking circle this content belongs to
  • consent_scope: Who has consented to access
  • elder_approval: Boolean + timestamp

Specific questions:

  • How to implement pre-filter in Upstash that respects all these constraints?
  • How to build a governance service for post-filter validation?
  • How to handle "seasonal windows" (e.g., Winter Solstice content only accessible Dec 21 - Jan 6)?
  • How to audit access attempts for community accountability?

Desired output: Schema definitions, query patterns, governance service architecture

4. Trauma-Informed Safety Rails

Request: Implementation patterns for hard-gating high-risk states to human pathways.

Based on TIC principles (safety, choice, collaboration, empowerment):

  • How to detect crisis states (suicidality, psychosis indicators) in conversation?
  • How to gracefully hand off to human support without abandonment?
  • How to implement "always offer choice" and "ability to pause/exit" in flow?
  • How to log safety events for Elder review without violating privacy?

Desired output: Detection patterns, handoff protocols, consent-preserving logging

5. Eight Feelings Stage-Specific Constraints

Request: Design pattern for implementing different conversational affordances for each Erikson stage.

Example constraints needed:

  • Trust (0-1y wound): No advice-pushing, focus on safety, slow pace
  • Autonomy (1-2y wound): Offer choices always, honor refusal, no shame
  • Identity (12-18y wound): No identity reframing without consent, hold space for ambiguity
  • Intimacy (19-30y wound): Clear boundaries, no dependency patterns, regular consent checks

Specific questions:

  • How to detect which stage the user's current wound relates to?
  • How to switch agent behavior based on detected stage?
  • How to track stage progression across multiple sessions?

Desired output: Stage detection heuristics, behavior constraint rules, session state schema

Technical Stack Context

  • Database: Neon (PostgreSQL)
  • Vector Store: Upstash
  • KV Store: Upstash Redis
  • Framework: Next.js / TypeScript
  • LLM: Claude (Anthropic) via API
  • Agent Framework: To be selected (LangGraph, CrewAI, or hybrid)
  • Existing MCPs: mcp-medicine-wheel, inquiries-knowledge-beats

Output Format

For each section, please provide:

  1. Architecture Overview (textual diagram or bullet structure)
  2. Key Components (what to build)
  3. Code Patterns (TypeScript/Python snippets or pseudocode)
  4. Integration Points (how it connects to our existing stack)
  5. Risks/Warnings (what could go wrong, especially ethically)
  6. References (papers, repos, docs to consult)

Constraints

  1. Trauma-informed throughout - Every pattern must honor TIC values
  2. OCAP®-compliant - Community control, not just access control
  3. Ceremony-respecting - Technology serves ceremony, not replaces it
  4. Human-primary - AI supports human healers, never replaces them
  5. Elder-approved pathways - All high-risk flows require human oversight

Priority

Highest: Vector store governance layer (blocking for v0.dev MARROW implementation) High: LangGraph Medicine Wheel architecture Medium: CrewAI Six Powers pattern Lower: Stage detection heuristics (can iterate)

This is the second phase of our inquiry. We honor the knowledge you've already shared and seek to deepen our implementation understanding.

Thank you.

Session ID: 260120-MISSION-260118075628-cont--20c06417-422d-4996-a0ff-404ec865980a Direction: Moving from EAST (vision) to SOUTH (implementation) ```

ACTION ITEMS FROM INITIAL SURVEY

Immediate Implementation (This Week)

  1. Formalize Vector Store Governance Layer

    • Add metadata fields: season, sacred_level, relation_nodes, circle_id, consent_scope, elder_approval_state
    • Implement two-step retrieval pattern
    • Reference: AWS RAG guidance, PAIG VectorDB policies

  2. Select LangGraph for Ceremony Spiral

    • Model Medicine Wheel as graph with cyclical flow
    • Structural tension as state management
    • Reference: LangGraph docs, openCHA architecture

  3. Lock in Trauma-Informed Guardrails

    • Translate TIC values to concrete rules
    • Stage-specific safety constraints
    • Reference: NSF TIC-chatbot project, Siddals et al. 2024

Documentation Updates

  1. Align README with Abundant Intelligences + Indigenous Protocol
    • Cite Lewis et al. 2020 in governance docs
    • Reference FNIGC/DSRC for OCAP compliance
    • Link to Rieger et al. as methodological lineage

Community Connections

  1. Reach out to identified organizations
    • Abundant Intelligences project (SSHRC-funded)
    • Indigenous Protocol and AI Working Group
    • FNIGC / Data Sovereignty Research Collaborative

KEY CITATIONS TO ADD TO DOCS

```bibtex @article{lewis2020indigenous, title={Indigenous Protocol and Artificial Intelligence}, author={Lewis, Jason Edward and Hernández, Noelani Arista and Benesiinaabandan, Suzanne and others}, year={2020}, publisher={Concordia University} }

@article{yolgormez2024abundant, title={Towards Abundant Intelligences: Considerations for Indigenous perspectives in adopting artificial intelligence technology}, author={Yolgörmez, Ceyda and others}, journal={SAGE Publications}, year={2024} }

@article{rieger2021digital, title={Digital Storytelling as a Patient Engagement and Research Method: Talking Circles and Debwewin Journey}, author={Rieger, K.L. and others}, journal={PMC}, year={2021} }

@article{harris2024ai, title={Artificial Intelligence in Human Growth and Development: Applications Through the Lifespan}, author={Harris, B. and others}, journal={SAGE Journals}, year={2024} } ```

SESSION CONTINUITY

Narrative Beat Created: chart_1768740253961_beat_1768916150301

  • Type: Turning Point / Discovery
  • Key lesson: We are doing original work at the intersection of Erikson + trauma + AI healing protocols

Next Direction Movement: EAST → SOUTH (Vision → Implementation)

Parallel Sessions:

  • XDoTool MCP validation: 1/13 tests passed (monitoring)
  • v0.dev MARROW/FAMC-KB: Awaiting governance layer spec from this inquiry

This document prepared for continuation of inquiry with Perplexity.ai Filed: 260120 13:35 UTC