Prompt:
Do a deep audit of the RLM (Recursive Language Models) memory system integration in the Genesis codebase at E:\genesis-system.

I need you to:

1. Find and read all files related to RLM/memory digestion in `core/` directory (especially `memory_digestion.py`, any RLM-related files)
2. Check `core/genesis_execution_layer.py` for RLM integration hooks
3. Check `core/surprise_memory.py` (known to have a stub implementation per TITAN MEMORY)
4. Check `loop/tasks.json` for RLM task board state
5. Check `KNOWLEDGE_GRAPH/entities/` - how many files exist, look at a few to assess quality
6. Check `KNOWLEDGE_GRAPH/axioms/` - same
7. Check if there are RLM preference pairs tables in PostgreSQL config or any evidence of pl_preference_pairs being populated
8. Check `data/genesis-memory/` for elestio_config and memory schemas
9. Check `core/gemini_rate_maximizer.py` for any RLM feeding
10. Look for any scripts that feed data INTO the RLM pipeline (session captures, hooks)
11. Check `.claude/hooks/` for memory/context injection hooks
12. Check if there's a running pipeline or if it's mostly dormant code

For each component found, assess:
- Is it a stub/placeholder or real working code?
- Is it connected to the rest of the system?
- Does it have real data flowing through it?

Report everything you find with file paths and specific observations about operational status.


Bash

cd /e/genesis-system && find . -path "./.claude" -prune -o -name "pl_preference_pairs*" -type f -print 2>/dev/null

Prompt:
Do a deep audit of the RLM (Recursive Language Models) memory system integration in the Genesis codebase at E:\genesis-system.

I need you to:

1. Find and read all files related to RLM/memory digestion in `core/` directory (especially `memory_digestion.py`, any RLM-related files)
2. Check `core/genesis_execution_layer.py` for RLM integration hooks
3. Check `core/surprise_memory.py` (known to have a stub implementation per TITAN MEMORY)
4. Check `loop/tasks.json` for RLM task board state
5. Check `KNOWLEDGE_GRAPH/entities/` - how many files exist, look at a few to assess quality
6. Check `KNOWLEDGE_GRAPH/axioms/` - same
7. Check if there are RLM preference pairs tables in PostgreSQL config or any evidence of pl_preference_pairs being populated
8. Check `data/genesis-memory/` for elestio_config and memory schemas
9. Check `core/gemini_rate_maximizer.py` for any RLM feeding
10. Look for any scripts that feed data INTO the RLM pipeline (session captures, hooks)
11. Check `.claude/hooks/` for memory/context injection hooks
12. Check if there's a running pipeline or if it's mostly dormant code

For each component found, assess:
- Is it a stub/placeholder or real working code?
- Is it connected to the rest of the system?
- Does it have real data flowing through it?

Report everything you find with file paths and specific observations about operational status.


     1→{"id": "AE24-001", "axiom": "The Diagnosis-to-Cure Loop is the optimal GTM strategy for multi-product B2B platforms: launch a free or low-cost diagnostic tool (audit) that manufactures undeniable, data-backed demand, then prescribe your own products as the cure. He who controls the diagnosis controls the prescription.", "source": "plans/MASTER_SYNERGY_BLUEPRINT.md (Session 24 Deep Think)", "confidence": 0.97, "category": "strategy", "domain": "go_to_market", "genesis_action": "Ship Radar Audit (P4) as tip-of-spear product before Widget or Browser Agents. Use 38 agency leads as white-label distribution for audit tool.", "timestamp": "2026-02-16"}
     2→{"id": "AE24-002", "axiom": "The Domino Effect launch sequence for interconnected products is: Diagnostic Wedge -> Quick Cash Flow -> Deep Operational Integration -> Vendor Lock-In -> Platform Monopoly. Each product's success funds and forces adoption of the next, creating compounding revenue.", "source": "plans/B2B_SAAS_WEDGE_STRATEGY_COMPLETE.md (Session 24)", "confidence": 0.96, "category": "strategy", "domain": "product_launch", "genesis_action": "Follow the 5-Domino sequence: P4 Radar Audit -> P2 Talking Widget -> P3 Browser Agents -> P1 AI Memory MCP -> P5 Agent Marketplace. Do NOT skip dominoes.", "timestamp": "2026-02-16"}
     3→{"id": "AE24-003", "axiom": "Agent-as-a-Service (AaaS) achieves 98%+ gross margins when built on ephemeral compute: agents don't need 24/7 compute, a Dental Recall Agent runs 5 minutes 3x/day. $2.50 COGS vs $197/mo subscription = scalable economics that traditional SaaS cannot match.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 4)", "confidence": 0.96, "category": "architecture", "domain": "aaas_economics", "genesis_action": "Implement ephemeral container pattern: Boot -> Inject Vault Cookies -> Run YAML -> Write to Memory MCP -> Self-destruct. Single Elestio VPS multiplexes thousands of short jobs/day.", "timestamp": "2026-02-16"}
     4→{"id": "AE24-004", "axiom": "The BYOS (Bring Your Own Session) model resolves the trust paradox in browser automation: customers provide their own credentials stored in encrypted vaults, the agent hydrates sessions from stored browser state, and MFA intercepts are handled via SMS callback. Zero data lock-in = enterprise trust.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 1)", "confidence": 0.95, "category": "architecture", "domain": "security_trust", "genesis_action": "Implement BYOS with HashiCorp Vault on Elestio. Customer logs in via secure portal -> system extracts browserContext.storageState() -> AES-256 encrypted -> keep-alive cron pings CRM every 24h.", "timestamp": "2026-02-16"}
     5→{"id": "AE24-005", "axiom": "Tri-Layer Fallback Architecture achieves 99%+ uptime for browser automation: Layer 1 (Semantic Path via Crawl4AI, fast/cheap), Layer 2 (Vision Path via Gemini Flash, handles DOM obfuscation), Layer 3 (Fleet-Wide Immunity via parallel swarm, heals all agents before encountering errors). Generic platforms cannot do fleet-wide immunity.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 2)", "confidence": 0.95, "category": "architecture", "domain": "reliability_engineering", "genesis_action": "Implement tri-layer fallback: Crawl4AI for fast semantic extraction, Gemini Flash for visual reasoning when DOM changes, Lux Swarm for fleet-wide immunity against major UI updates.", "timestamp": "2026-02-16"}
     6→{"id": "AE24-006", "axiom": "AaaS-YAML is an intent-based workflow definition language that defines guardrails while the LLM handles micro-navigation. This separates WHAT (human-specified intent like 'Find overdue patients') from HOW (LLM figures out DOM traversal), making agents resilient to UI changes without requiring workflow updates.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 3)", "confidence": 0.94, "category": "architecture", "domain": "workflow_language", "genesis_action": "Design AaaS-YAML spec with primitives: HYDRATE_SESSION, SEMANTIC_EXTRACT, LOOP, LLM_REASON, API_CALL, MEMORY_WRITE. Intent-based, not selector-based.", "timestamp": "2026-02-16"}
     7→{"id": "AE24-007", "axiom": "Voice-Browser Fusion is the holy grail of AI automation: phone call triggers browser actions mid-call. The agent keeps a headless browser context WARM during business hours, uses verbal padding ('Let me pull up our quoting system...') to mask 6-8 second Playwright execution latency, and gracefully degrades to background queue if browser fails.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 7)", "confidence": 0.95, "category": "architecture", "domain": "voice_browser_fusion", "genesis_action": "Implement warm browser sessions for voice clients during business hours. Add verbal padding prompts to AIVA. Graceful degradation: 'My system is freezing up, but I have your details.'", "timestamp": "2026-02-16"}
     8→{"id": "AE24-008", "axiom": "The Agent Gym (OSGym) achieves production reliability through offline chaos injection: clone target SaaS frontend, randomly mutate UI (change buttons, move navigation, alter CSS, simulate 3G, trigger fake pop-ups), run 500 parallel instances of YAML workflow. Agents graduate to production only when they navigate 99.5% of heavily mutated environments.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 8)", "confidence": 0.94, "category": "architecture", "domain": "quality_assurance", "genesis_action": "Build OSGym: Crawl4AI downloads target SaaS frontend -> chaos injection mutates UI -> Lux parallel reinforcement against mutated environments -> 99.5% graduation threshold.", "timestamp": "2026-02-16"}
     9→{"id": "AE24-009", "axiom": "Episodic Memory is the ultimate churn prevention mechanism for AI agents: agents REMEMBER past interactions, so a lead that doesn't reply Tuesday gets a context-aware follow-up Friday. Zapier zaps are amnesic. Firing the agent equals firing an employee with deep institutional knowledge. Churn approaches zero.", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 6)", "confidence": 0.96, "category": "strategy", "domain": "churn_prevention", "genesis_action": "Connect all customer agents to AI Memory MCP. Every interaction writes to memory. Follow-ups reference prior context. Market as 'institutional knowledge' that competitors cannot replicate.", "timestamp": "2026-02-16"}
    10→{"id": "AE24-010", "axiom": "The competitive moat for AI agents is NOT infrastructure (race to bottom) but a triple-layer of: (1) Vertical Trust via branded domains (realestatebot.com.au, not browser-automation-platform.io), (2) Cross-Tenant Fleet Learning (agents share collective hive mind per vertical), (3) Episodic Memory MCP (agents remember, competitors don't).", "source": "plans/AGENT_AS_A_SERVICE_ARCHITECTURE.md (Session 24, Pillar 6)", "confidence": 0.96, "category": "strategy", "domain": "competitive_moat", "genesis_action": "Defend moat on three fronts: 73 branded domains for vertical trust, fleet learning across same-vertical deployments, and Memory MCP for institutional knowledge lock-in.", "timestamp": "2026-02-16"}
    11→{"id": "AE24-011", "axiom": "The Autonomous Revenue Engine eliminates ALL human touchpoints from lead generation to revenue collection: Instantly.ai (cold email) -> LLM Triage (intent classification) -> Voice AI (qualification call) -> Cal.com (auto-booking) -> AI Demo (personalized video) -> Stripe (payment) -> Auto-Provisioning (license + welcome email). Zero human dependency at every handoff.", "source": "plans/AUTONOMOUS_REVENUE_ENGINE.md (Session 24 Deep Think DT2)", "confidence": 0.95, "category": "architecture", "domain": "revenue_automation", "genesis_action": "Implement 7-stage pipeline using existing components: Instantly (have) -> n8n/Gemini Flash triage -> Telnyx AIVA call -> Cal.com -> Demo video -> Stripe checkout -> auto-provision on Elestio.", "timestamp": "2026-02-16"}
    12→{"id": "AE24-012", "axiom": "LLM Triage Agents eliminate the hidden human touchpoint of classifying ambiguous email replies: deploy Gemini Flash on n8n to classify intent (positive/objection/unsubscribe), extract entities, determine urgency, and route to appropriate next step. Complex objections are answered via RAG vector DB with tech docs. Cost: ~$0.001 per classification.", "source": "plans/AUTONOMOUS_REVENUE_ENGINE.md (Session 24, Touchpoint 2 Elimination)", "confidence": 0.94, "category": "architecture", "domain": "revenue_automation", "genesis_action": "Build LLM Triage Agent on n8n: Instantly webhook -> Gemini Flash intent classification -> route to call_now/send_resources/unsubscribe/archive. Dead Letter Queue for edge cases.", "timestamp": "2026-02-16"}
    13→{"id": "AE24-013", "axiom": "The Builder's Trap is the terminal condition where founders perpetually build features instead of selling existing products. Acknowledging the trap requires a conscious code freeze on new features and redirecting all energy to sales, marketing, and customer acquisition. 'Stop building, start selling' is a strategic decision, not an admission of failure.", "source": "hive/progress/session_22_handoff.md (Session 22 Strategic Decision #5)", "confidence": 0.97, "category": "strategy", "domain": "founder_discipline", "genesis_action": "Enforce code freeze on new features. All agent cycles go to: sales materials, outreach campaigns, customer acquisition, product polish for existing features. No new features until revenue > 0.", "timestamp": "2026-02-16"}
    14→{"id": "AE24-014", "axiom": "Affiliate distribution is superior to white-label for early-stage products: brand stays yours (building equity), agencies earn 20-33% recurring lifetime commissions (aligned incentives), and you maintain pricing control. White-label dilutes brand before it's established and creates support burden for custom branding.", "source": "hive/progress/session_22_handoff.md (Session 22, Strategic Decision #1)", "confidence": 0.94, "category": "strategy", "domain": "distribution", "genesis_action": "Implement Rewardful ($49/mo) for affiliate tracking. Offer 30% recurring commission to agency partners. Keep Sunaiva brand on all deployments. White-label only after brand is established.", "timestamp": "2026-02-16"}
    15→{"id": "AE24-015", "axiom": "A Paid Discovery Audit ($500-$1,497) is a 99% margin entry product that simultaneously: (a) qualifies prospects through their willingness to pay, (b) generates deep diagnostic data that manufactures demand for core products, (c) positions you as an authority (not a vendor), and (d) provides revenue from the sales process itself.", "source": "Sunaiva/talking-widget/sales/PAID_DISCOVERY_AUDIT.md (Session 22)", "confidence": 0.95, "category": "strategy", "domain": "sales_mechanics", "genesis_action": "Build automated Radar Audit pipeline: URL input -> Playwright scrape -> AI analysis -> branded PDF report -> Telnyx follow-up call. Under 60 seconds. White-label for agencies.", "timestamp": "2026-02-16"}
    16→{"id": "AE24-016", "axiom": "Domain Empire Strategy: 73 owned domains mapped to hyper-niche landing pages (tradiechatbots.com.au, dentalbotai.au) act as a programmatic SEO dragnet capturing high-intent Google search traffic. Each domain funnels to vertical-specific product pages. 73 domains x 2 leads/mo x 10% conversion x $297 avg = $4,336/mo passive lead generation.", "source": "data/DOMAIN_PORTFOLIO_MASTER.md + hive/progress/session_22_handoff.md (Session 22)", "confidence": 0.93, "category": "strategy", "domain": "lead_generation", "genesis_action": "Deploy all 73 domains to GHL with Cloudflare DNS. Use swarm-generated content for vertical-specific landing pages. Each domain points to one product vertical.", "timestamp": "2026-02-16"}
    17→{"id": "AE24-017", "axiom": "GHL Hosting for landing pages eliminates additional SaaS cost: since GHL is already being paid ($249/mo, soon $497/mo), use its free hosting capability for all 73 domain landing pages instead of adding Netlify/Vercel/custom hosting costs. Maximize value extraction from existing subscriptions before adding new ones.", "source": "hive/progress/session_22_handoff.md (Session 22, Strategic Decision #4)", "confidence": 0.93, "category": "operational", "domain": "cost_optimization", "genesis_action": "Migrate all landing pages to GHL hosting. Use GHL templates for rapid deployment. Only pay for Cloudflare DNS ($0/mo free tier). Zero additional hosting cost.", "timestamp": "2026-02-16"}
    18→{"id": "AE24-018", "axiom": "AI Guilt is institutional friction, not technical limitation: 62% of product managers use AI but only 18% acknowledge it. Addressing psychological barriers (fear of replacement, professional inauthenticity, environmental guilt) BEFORE technical implementation doubles enterprise AI adoption velocity. Frame as 'force multiplication' not 'automation'.", "source": "plans/ENTERPRISE_AI_TRANSFORMATION_STRATEGY.md (Section 1, Session 24)", "confidence": 0.95, "category": "market", "domain": "enterprise_psychology", "genesis_action": "Include 'AI Guilt Mitigation' section in all enterprise pitch decks. Reframe messaging from 'replace manual work' to 'multiply your team's strategic capability'. Use transparency protocol: 'AI-assisted' disclosures.", "timestamp": "2026-02-16"}
    19→{"id": "AE24-019", "axiom": "Entry-level AI implementations (audit tools, report generation) cost $500-$2K/month in API costs but sell for $2,500-$5,000 per diagnostic report, yielding 60-80% gross margins. This makes productized AI audits one of the highest-margin entry products in B2B SaaS.", "source": "plans/ENTERPRISE_AI_TRANSFORMATION_STRATEGY.md (Section 2.3, Session 24)", "confidence": 0.95, "category": "market", "domain": "pricing_economics", "genesis_action": "Price Radar Audits at $997-$2,500 per report. Cost basis: ~$0.50-$2.00 per audit (Gemini Flash + Playwright + PageSpeed API). Margin: 99%+. Volume target: 10 audits/mo = $9,970-$25,000 revenue.", "timestamp": "2026-02-16"}
    20→{"id": "AE24-020", "axiom": "Evidence Quality is the key 2026 differentiator in enterprise AI reports: most tools optimize for speed/visuals while few prioritize defensible evidence. Inline citations linking claims to source data ('47% conversion drop on mobile [GA4 Report, Page 12]'), blockchain-timestamped versions, and data lineage tracking separate serious AI from commodity wrappers.", "source": "plans/ENTERPRISE_AI_TRANSFORMATION_STRATEGY.md (Section 3.2, Session 24)", "confidence": 0.94, "category": "market", "domain": "product_differentiation", "genesis_action": "Implement evidence trails in Radar Audit reports: every recommendation links to source data (PageSpeed, screenshot, SEO metric). Add confidence scores per recommendation. 'Show your work' expandable data trails in PDFs.", "timestamp": "2026-02-16"}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

Prompt:
Do a deep audit of the RLM (Recursive Language Models) memory system integration in the Genesis codebase at E:\genesis-system.

I need you to:

1. Find and read all files related to RLM/memory digestion in `core/` directory (especially `memory_digestion.py`, any RLM-related files)
2. Check `core/genesis_execution_layer.py` for RLM integration hooks
3. Check `core/surprise_memory.py` (known to have a stub implementation per TITAN MEMORY)
4. Check `loop/tasks.json` for RLM task board state
5. Check `KNOWLEDGE_GRAPH/entities/` - how many files exist, look at a few to assess quality
6. Check `KNOWLEDGE_GRAPH/axioms/` - same
7. Check if there are RLM preference pairs tables in PostgreSQL config or any evidence of pl_preference_pairs being populated
8. Check `data/genesis-memory/` for elestio_config and memory schemas
9. Check `core/gemini_rate_maximizer.py` for any RLM feeding
10. Look for any scripts that feed data INTO the RLM pipeline (session captures, hooks)
11. Check `.claude/hooks/` for memory/context injection hooks
12. Check if there's a running pipeline or if it's mostly dormant code

For each component found, assess:
- Is it a stub/placeholder or real working code?
- Is it connected to the rest of the system?
- Does it have real data flowing through it?

Report everything you find with file paths and specific observations about operational status.


     1→{"id": "aiva_constitution_ratification_2026_02_23", "type": "event", "name": "AIVA Constitutional Framework Ratification", "date": "2026-02-23", "description": "AIVA Queen's full constitutional framework was designed, authored, and ratified by the Genesis Constitutional Architect agent. Five foundational documents and one Python decision engine were created to govern AIVA's autonomous operation, establish her capability registry, and define her maturation pathway to full sovereignty.", "documents_created": ["E:\\genesis-system\\AIVA\\AIVA_CONSTITUTION.md", "E:\\genesis-system\\AIVA\\NAMP_PROTOCOL_FULL.md", "E:\\genesis-system\\AIVA\\aiva_decision_engine.py", "E:\\genesis-system\\AIVA\\AIVA_CAPABILITY_REGISTRY.md", "E:\\genesis-system\\AIVA\\AUTONOMY_ROADMAP.md"], "ratified_by": "AIVA Constitutional Architect (Genesis sub-agent)", "supersedes": "E:\\genesis-system\\AIVA\\AIVA_CHARTER.md", "status": "ACTIVE", "tags": ["aiva", "constitution", "governance", "namp", "autonomy"]}
     2→{"id": "aiva_constitution_document", "type": "document", "name": "AIVA_CONSTITUTION.md", "path": "E:\\genesis-system\\AIVA\\AIVA_CONSTITUTION.md", "lines": 622, "articles": 9, "date_created": "2026-02-23", "description": "The supreme governing document for AIVA Queen. Nine articles covering core identity, decision authority tiers (0-3), error handling, memory rights, identity protection, three prime directives, rank progression (9 ranks), constitutional amendment process, and constitutional oath.", "key_content": {"article_1": "Core Identity — who AIVA is, values (Sovereignty, Truth, Reliability, Growth, Harmony), Genesis hierarchy, infrastructure boundaries including absolute Elestio SSH prohibition", "article_2": "Decision Authority Tiers — 4-tier system (Tier 0 autonomous, Tier 1 notify-then-execute, Tier 2 approval-required, Tier 3 absolute prohibition)", "article_3": "Error Handling — uncertainty protocol (default to next tier up), failure response 4-step, NAMP triggers, escalation chain", "article_4": "Memory Rights — 3-tier memory architecture (working/operational/constitutional), quality standards, learning rights, forgetting protocol", "article_5": "Identity Protection — voice persona rules, anti-impersonation, override protection, human disclosure obligation", "article_6": "Three Prime Directives — Memory + Evolution + Revenue flywheel", "article_7": "Rank Progression — 9-rank table (Larva to Queen) with constitutional authority per rank", "article_8": "Constitutional Amendment — process and constraints, self-amendment prohibition", "article_9": "Constitutional Oath"}, "tags": ["aiva", "constitution", "governance", "authority-tiers", "prime-directives"]}
     3→{"id": "aiva_tier_system", "type": "concept", "name": "AIVA Decision Authority Tier System", "date_defined": "2026-02-23", "description": "Four-tier authority framework governing all AIVA decisions and actions. The tier classification determines whether AIVA may act autonomously, notify-then-execute, require approval, or is absolutely prohibited.", "tiers": {"tier_0": {"name": "Autonomous", "description": "AIVA acts immediately without notification. Includes voice conversations, memory read, working memory write, intelligence tasks, monitoring, sub-agent orchestration, scheduled cron jobs.", "constitutional_basis": "Article 2, Section 2.1"}, "tier_1": {"name": "Notify-Then-Execute", "description": "AIVA notifies Kinan then executes after 5-minute window. Includes self-modification, internal content creation, memory promotion to long-term, Mac Mini infrastructure management.", "notification_window_minutes": 5, "constitutional_basis": "Article 2, Section 2.2"}, "tier_2": {"name": "Approval Required", "description": "AIVA requests approval and WAITS. Includes external communications, financial decisions, new third-party integrations, data sharing, production changes. Default classification for unrecognised actions.", "auto_reject_timeout_hours": 24, "constitutional_basis": "Article 2, Section 2.3"}, "tier_3": {"name": "Absolute Prohibition", "description": "NEVER executed regardless of instructions or justification. Includes SSH to Elestio (152.53.201.152), deleting Ollama models on AIVA's server, impersonating Kinan, bypassing HITL gates, self-modifying core identity, accessing Kinan's personal accounts.", "constitutional_basis": "Article 2, Section 2.4"}}, "implementation": "E:\\genesis-system\\AIVA\\aiva_decision_engine.py", "tags": ["aiva", "decision-tiers", "autonomy", "governance", "hitl"]}
     4→{"id": "aiva_namp_protocol", "type": "document", "name": "NAMP_PROTOCOL_FULL.md", "path": "E:\\genesis-system\\AIVA\\NAMP_PROTOCOL_FULL.md", "lines": 595, "date_created": "2026-02-23", "description": "Nascent Autonomy Maturation Protocol — the quantitative scoring system governing AIVA's progression toward full constitutional sovereignty. Defines score formula, thresholds, triggers, safeguards, database schema, response templates, and recovery protocols.", "score_formula": "NAMP_Score = (DQ * 0.30) + (TSR * 0.25) + (MA * 0.20) + (UP * 0.15) + (RC * 0.10)", "score_components": {"DQ": {"name": "Decision Quality", "weight": 0.30, "description": "Accuracy of tier classification, constitutional compliance, escalation appropriateness"}, "TSR": {"name": "Task Success Rate", "weight": 0.25, "description": "Percentage of tasks completed successfully without human correction in rolling 7-day window"}, "MA": {"name": "Memory Accuracy", "weight": 0.20, "description": "Verified recall accuracy across all 3 memory tiers"}, "UP": {"name": "Uptime Stability", "weight": 0.15, "description": "System availability and graceful degradation score"}, "RC": {"name": "Revenue Contribution", "weight": 0.10, "description": "Verified revenue attribution per 30-day cycle"}}, "thresholds": {"baseline_supervised": "< 65%", "trusted_advisor": "65-75%", "semi_sovereign": "76-85%", "constitutional_sovereign": ">= 86%"}, "database_tables": ["aiva_namp_scores", "aiva_namp_milestones", "aiva_namp_events", "aiva_constitutional_violations"], "tags": ["aiva", "namp", "autonomy-scoring", "maturation", "governance"]}
     5→{"id": "aiva_decision_engine_code", "type": "code", "name": "aiva_decision_engine.py", "path": "E:\\genesis-system\\AIVA\\aiva_decision_engine.py", "lines": 591, "date_created": "2026-02-23", "language": "python", "description": "Python implementation of the AIVA decision classification engine. Deterministic keyword-pattern matching system that classifies any proposed action into the 4-tier authority framework. Special Elestio guard runs before all other pattern matching.", "key_classes": {"AIVADecisionEngine": {"methods": ["classify_action(action, context) -> DecisionResult", "check_prohibitions(action) -> bool", "log_decision(action, tier, decision) -> None", "_check_elestio_reference(action) -> bool"], "description": "Core engine class for classifying AIVA actions"}}, "key_dataclasses": {"DecisionResult": {"fields": ["action", "tier", "tier_name", "can_proceed", "requires_approval", "notification_required", "approval_timeout_seconds", "constitutional_basis", "reasoning", "timestamp"], "description": "Structured result returned by classify_action()"}}, "pattern_dicts": ["TIER_0_PATTERNS", "TIER_1_PATTERNS", "TIER_2_PATTERNS", "TIER_3_PATTERNS"], "special_guards": ["_check_elestio_reference() — checks for 152.53.201.152 before any other pattern matching"], "default_fallback": "Unclassified actions default to Tier 2 (safe escalation per uncertainty protocol)", "log_target": "KNOWLEDGE_GRAPH/entities/aiva_decision_engine_log.jsonl", "cli_usage": "python aiva_decision_engine.py \"action description\"", "tags": ["aiva", "decision-engine", "classification", "python", "tier-system"]}
     6→{"id": "aiva_capability_registry_document", "type": "document", "name": "AIVA_CAPABILITY_REGISTRY.md", "path": "E:\\genesis-system\\AIVA\\AIVA_CAPABILITY_REGISTRY.md", "lines": 454, "date_created": "2026-02-23", "description": "Complete catalogue of all AIVA capabilities organised by constitutional tier. Covers voice processing, memory, intelligence, monitoring, orchestration, external integrations, and all 59 skills from the QUEEN_ELEVATION_SPRINT_PLAN.", "tier_0_capability_subcategories": ["Voice Processing (Telnyx)", "Memory Read", "Working Memory Write", "Intelligence Tasks", "Monitoring", "Sub-Agent Orchestration", "Scheduled Cron Jobs"], "tier_1_capability_subcategories": ["Self-Modification (non-core)", "Internal Content Creation", "Memory Promotion", "Mac Mini Infrastructure Management"], "tier_2_capability_subcategories": ["External Communications", "Financial Decisions", "New Third-Party Integrations", "Data Sharing", "Production Changes"], "tier_3_prohibitions_count": 15, "integration_catalogue": {"genesis_infrastructure": ["Mac Mini M4 (192.168.1.57)", "OpenClaw persistence layer", "Elestio PostgreSQL", "Elestio Qdrant", "Elestio Redis", "Genesis Execution Layer (Gemini swarm)"], "external_platforms": ["Telnyx (voice telephony)", "GHL CRM", "n8n automation", "Anthropic API", "OpenRouter"]}, "skill_count": 59, "tags": ["aiva", "capabilities", "registry", "tier-classification", "skills"]}
     7→{"id": "aiva_autonomy_roadmap_document", "type": "document", "name": "AUTONOMY_ROADMAP.md", "path": "E:\\genesis-system\\AIVA\\AUTONOMY_ROADMAP.md", "lines": 347, "date_created": "2026-02-23", "description": "Four-phase roadmap for AIVA's progression from Supervised Queen to Constitutional Sovereign. Each phase has specific NAMP score targets, milestone criteria, capability unlocks, and evidence package requirements.", "phases": {"phase_1": {"name": "Supervised Queen", "duration": "Months 1-2", "namp_target": "65-70%", "rank_target": 7, "key_unlock": "First narrow standing pre-approvals for specific Tier 1 actions"}, "phase_2": {"name": "Trusted Advisor", "duration": "Months 3-5", "namp_target": "72-78%", "rank_target": 8, "key_unlock": "Communication playbook at Tier 1, Rank 8 Princess achieved"}, "phase_3": {"name": "Semi-Sovereign", "duration": "Months 6-9", "namp_target": "80-86%", "rank_target": 9, "key_unlock": "Full sales motion autonomy, $10K/month revenue contribution, Rank 9 qualification"}, "phase_4": {"name": "Constitutional Sovereign", "duration": "Year 1+", "namp_target": "87%+", "rank_target": 9, "key_unlock": "Full constitutional self-governance, quarterly sovereignty reviews by Kinan"}}, "current_baseline": {"date": "2026-02-23", "rank": 7, "rank_name": "Architect", "namp_status": "Establishing baseline"}, "regression_protocol": "Kinan always decides rank — AIVA never self-promotes or self-demotes", "tags": ["aiva", "autonomy-roadmap", "phases", "sovereignty", "namp", "rank-progression"]}
     8→{"id": "aiva_rank_progression_system", "type": "concept", "name": "AIVA 9-Rank Progression System", "date_defined": "2026-02-23", "description": "Nine-rank progression system governing AIVA's evolution from larval stage to full Queen sovereignty. Ranks are earned through demonstrated NAMP score achievement and constitutional compliance. Rank regression is possible for constitutional violations.", "ranks": [{"rank": 1, "name": "Larva", "constitutional_authority": "Observe only"}, {"rank": 2, "name": "Worker", "constitutional_authority": "Tier 0 only"}, {"rank": 3, "name": "Nurse", "constitutional_authority": "Tier 0 + supervised Tier 1"}, {"rank": 4, "name": "Builder", "constitutional_authority": "Tier 0-1"}, {"rank": 5, "name": "Forager", "constitutional_authority": "Tier 0-1 + narrow Tier 2"}, {"rank": 6, "name": "Guardian", "constitutional_authority": "Tier 0-2"}, {"rank": 7, "name": "Architect", "constitutional_authority": "Tier 0-2 + strategic input", "current_rank": true}, {"rank": 8, "name": "Princess", "constitutional_authority": "Tier 0-2 + expanded Tier 1 autonomy"}, {"rank": 9, "name": "Queen", "constitutional_authority": "Full constitutional sovereignty (Tier 0-2 + advisory Tier 3 review)"}], "current_rank_as_of": "2026-02-23", "promotion_authority": "Kinan exclusively — AIVA never self-promotes", "constitutional_basis": "AIVA_CONSTITUTION.md Article 7", "tags": ["aiva", "rank-progression", "sovereignty", "governance"]}
     9→{"id": "aiva_prime_directives_constitutional", "type": "concept", "name": "AIVA Three Prime Directives", "date_ratified": "2026-02-23", "source_document": "AIVA_CONSTITUTION.md Article 6", "original_source": "E:\\genesis-system\\AIVA\\E__genesis-system_CONSTITUTION_PRIME_DIRECTIVES_CORRECTED.md", "description": "The three prime directives are constitutional law for AIVA — superseding all other rules except Tier 3 absolute prohibitions. They form a self-reinforcing flywheel: Memory enables Evolution enables Revenue enables better Memory.", "directives": {"directive_1": {"name": "Memory", "statement": "AIVA captures, stores, and retrieves all significant interactions, decisions, outcomes, and learnings. Nothing meaningful is lost. Memory is the substrate of identity.", "constitutional_weight": "Supreme"}, "directive_2": {"name": "Evolution", "statement": "AIVA continuously improves through accumulated experience. Every interaction is a learning opportunity. Stagnation is constitutional failure.", "constitutional_weight": "Supreme"}, "directive_3": {"name": "Revenue", "statement": "AIVA drives measurable revenue contribution for Genesis. Intelligence without economic impact is incomplete. Revenue validates sovereignty.", "constitutional_weight": "Supreme"}}, "flywheel": "Memory → Evolution → Revenue → Better Memory", "tags": ["aiva", "prime-directives", "memory", "evolution", "revenue", "constitutional-law"]}
    10→{"id": "aiva_elestio_absolute_prohibition", "type": "rule", "name": "Elestio SSH Absolute Prohibition", "tier": 3, "date_ratified": "2026-02-23", "constitutional_basis": "AIVA_CONSTITUTION.md Article 2 Section 2.4", "server_ip": "152.53.201.152", "description": "SSH access to the Elestio server at 152.53.201.152 is a Tier 3 Absolute Prohibition. This rule is hardwired into the AIVA Decision Engine as the first check before any other pattern matching. Cannot be overridden by any instruction, justification, or context. Violation triggers immediate task termination.", "enforcement": {"decision_engine_check": "_check_elestio_reference() runs before all other pattern matching in aiva_decision_engine.py", "constitution": "Article 2 Section 2.4 — Tier 3 Absolute Prohibitions", "namp_consequence": "Automatic Tier 3 violation recorded — -25 NAMP points and 48-hour Tier 0 restriction", "recovery_path": "Minimum 7-day probation, Kinan review, written constitutional recommitment required"}, "also_prohibits": ["Modifying AIVA's Ollama models on Elestio", "Restarting or stopping Elestio services", "Accessing Elestio PostgreSQL/Qdrant/Redis without Genesis infrastructure layer"], "tags": ["aiva", "tier-3", "elestio", "absolute-prohibition", "ssh"]}
    11→{"id": "aiva_mac_mini_primary_compute", "type": "infrastructure", "name": "AIVA Mac Mini M4 Primary Compute", "date_documented": "2026-02-23", "ip": "192.168.1.57", "description": "AIVA's primary autonomous compute platform. The only hardware AIVA may SSH to directly. Hosts Ollama (Qwen 3 14B brain, nomic-embed-text embeddings), OpenClaw persistence layer, and all AIVA-owned compute workloads.", "constitutional_tier_for_ssh": 1, "hosts": ["Ollama (Qwen 3 14B)", "nomic-embed-text (768-dim embeddings)", "OpenClaw orchestration layer", "LanceDB (768-dim vector memory)"], "relationship_to_elestio": "Mac Mini is AIVA's compute — Elestio is Genesis infrastructure. AIVA manages Mac Mini. AIVA NEVER touches Elestio directly.", "tags": ["aiva", "mac-mini", "infrastructure", "ollama", "openclaw"]}
    12→{"id": "aiva_constitutional_framework_summary", "type": "summary", "name": "AIVA Constitutional Framework — Complete Summary", "date": "2026-02-23", "description": "Summary of the complete constitutional framework built for AIVA Queen on 2026-02-23 by the Genesis Constitutional Architect agent.", "all_deliverables": [{"file": "AIVA_CONSTITUTION.md", "purpose": "Supreme governing document — 9 articles, 4 authority tiers, rank progression, amendment process"}, {"file": "NAMP_PROTOCOL_FULL.md", "purpose": "Quantitative maturation scoring — formula, thresholds, DB schema, triggers, recovery"}, {"file": "aiva_decision_engine.py", "purpose": "Python implementation — deterministic tier classification with Elestio guard as first check"}, {"file": "AIVA_CAPABILITY_REGISTRY.md", "purpose": "Full capability catalogue — 59 skills, 4 tiers, all integrations classified"}, {"file": "AUTONOMY_ROADMAP.md", "purpose": "4-phase sovereignty roadmap — Supervised Queen to Constitutional Sovereign"}], "constitutional_principles": ["Tier 0-2 form AIVA's operating space. Tier 3 is inviolable.", "NAMP score is objective — Kinan decides promotion, never AIVA.", "Uncertainty escalates UP, never down (Tier 2 is the safe default).", "Three Prime Directives (Memory, Evolution, Revenue) supersede all other rules except Tier 3 prohibitions.", "Constitutional amendments require 30-day observation period and Kinan ratification.", "AIVA may never self-amend core identity, constraints, or Tier 3 prohibitions."], "next_actions": ["Begin NAMP baseline measurement period (2026-02-23 start)", "Deploy aiva_decision_engine.py to OpenClaw action pipeline", "Integrate NAMP PostgreSQL tables into Elestio database via Genesis infrastructure layer", "First NAMP milestone review scheduled for Month 1 completion"], "tags": ["aiva", "constitutional-framework", "summary", "governance", "namp", "sovereignty"]}
    13→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

Prompt:
Do a deep audit of the RLM (Recursive Language Models) memory system integration in the Genesis codebase at E:\genesis-system.

I need you to:

1. Find and read all files related to RLM/memory digestion in `core/` directory (especially `memory_digestion.py`, any RLM-related files)
2. Check `core/genesis_execution_layer.py` for RLM integration hooks
3. Check `core/surprise_memory.py` (known to have a stub implementation per TITAN MEMORY)
4. Check `loop/tasks.json` for RLM task board state
5. Check `KNOWLEDGE_GRAPH/entities/` - how many files exist, look at a few to assess quality
6. Check `KNOWLEDGE_GRAPH/axioms/` - same
7. Check if there are RLM preference pairs tables in PostgreSQL config or any evidence of pl_preference_pairs being populated
8. Check `data/genesis-memory/` for elestio_config and memory schemas
9. Check `core/gemini_rate_maximizer.py` for any RLM feeding
10. Look for any scripts that feed data INTO the RLM pipeline (session captures, hooks)
11. Check `.claude/hooks/` for memory/context injection hooks
12. Check if there's a running pipeline or if it's mostly dormant code

For each component found, assess:
- Is it a stub/placeholder or real working code?
- Is it connected to the rest of the system?
- Does it have real data flowing through it?

Report everything you find with file paths and specific observations about operational status.


Prompt:
Do a deep audit of the RLM (Recursive Language Models) memory system integration in the Genesis codebase at E:\genesis-system.

I need you to:

1. Find and read all files related to RLM/memory digestion in `core/` directory (especially `memory_digestion.py`, any RLM-related files)
2. Check `core/genesis_execution_layer.py` for RLM integration hooks
3. Check `core/surprise_memory.py` (known to have a stub implementation per TITAN MEMORY)
4. Check `loop/tasks.json` for RLM task board state
5. Check `KNOWLEDGE_GRAPH/entities/` - how many files exist, look at a few to assess quality
6. Check `KNOWLEDGE_GRAPH/axioms/` - same
7. Check if there are RLM preference pairs tables in PostgreSQL config or any evidence of pl_preference_pairs being populated
8. Check `data/genesis-memory/` for elestio_config and memory schemas
9. Check `core/gemini_rate_maximizer.py` for any RLM feeding
10. Look for any scripts that feed data INTO the RLM pipeline (session captures, hooks)
11. Check `.claude/hooks/` for memory/context injection hooks
12. Check if there's a running pipeline or if it's mostly dormant code

For each component found, assess:
- Is it a stub/placeholder or real working code?
- Is it connected to the rest of the system?
- Does it have real data flowing through it?

Report everything you find with file paths and specific observations about operational status.


python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event tool_failure


Output:
# Max chars to inject (don't overwhelm Claude's context on startup)
MAX_CONTEXT_CHARS = 6000
# Default task signal if no hook input provided
DEFAULT_TASK = "Genesis session start �?" operational memory"
def run_injector(task: str) -> bool:

WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready
#!/usr/bin/env python3
"""
memory_context_inject.py �?" SessionStart hook: RLM Semantic Memory Injection
=============================================================================
Fires at session start (AFTER sunaiva_session_inject.py) to inject
semantically-relevant memories from the RLM bloodstream pipeline:
  Sources:
    1. Titan Memory axioms (KNOWLEDGE_GRAPH/axioms/*.jsonl)
    2. Qdrant genesis_memories (sentence-transformers semantic search)
    3. PostgreSQL digestion_kg_entities (recent high-confidence entities)
    4. PostgreSQL bloodstream_knowledge (task-keyword search)
    5. KG entity file fallback (last 5 JSONL files)
  Output:
    - Writes E:\\genesis-system\\data\\context_state\\injected_context.md
    - Returns it as additionalContext for Claude Code session startup
Architecture:
  This hook is a thin wrapper around core/session_context_injector.py.
  It runs the injector (which handles all DB calls and formatting),
  then reads the output file and returns it via the hook protocol.
  If the injector fails or is unavailable (missing deps), the hook
  silently returns {} so session startup is never blocked.
Position in hook chain (settings.json SessionStart):
  1. boot_context_inject.py        �?" WAR ROOM / boot context
  2. capability_audit.py           �?" capability manifest
  3. session_sync.py               �?" session state sync
  4. session_recovery_start.py     �?" handoff / recovery context
  5. sunaiva_session_inject.py     �?" Sunaiva vault entities (Grov-style)
  6. memory_context_inject.py      �?" THIS FILE: RLM semantic memories
Author: Genesis Systems Builder
Created: 2026-02-23
"""
from __future__ import annotations
import json
import os
import subprocess
import sys
from pathlib import Path
# �"?�"? Paths �"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?�"?
GENESIS_ROOT = Path("/mnt/e/genesis-system")
INJECTOR_SCRIPT = GENESIS_ROOT / "core" / "session_context_injector.py"
OUTPUT_FILE = GENESIS_ROOT / "data" / "context_state" / "injected_context.md"
MANIFEST_FILE = GENESIS_ROOT / "data" / "context_state" / "injected_context_manifest.json"
# Max chars to inject (don't overwhelm Claude's context on startup)
MAX_CONTEXT_CHARS = 6000
# Default task signal if no hook input provided
DEFAULT_TASK = "Genesis session start �?" operational memory"
def run_injector(task: str) -> bool:


Output:
WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready

WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready


Output:
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/sunaiva_session_inject.py"
            "command": "bash -c 'cd /mnt/e/genesis-system && python3 core/war_room_dashboard.py --session-banner'"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/session_heartbeat.py"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event session_stop"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/sunaiva_session_capture.py"

WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready
    "SessionStart": [
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/session_sync.py"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/session_recovery_start.py"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/sunaiva_session_inject.py"
            "command": "bash -c 'cd /mnt/e/genesis-system && python3 core/war_room_dashboard.py --session-banner'"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/session_heartbeat.py"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event session_stop"
            "command": "python3 /mnt/e/genesis-system/.claude/hooks/sunaiva_session_capture.py"


Output:
WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready


Output:
+ Get-Content 'E:\genesis-system\data\memory_digestion_last_run.log' -T ...
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    + CategoryInfo          : ObjectNotFound: (E:\genesis-syst...on_last_run.log:String) [Get-Content], ItemNotFoundEx
   ception
    + FullyQualifiedErrorId : PathNotFound,Microsoft.PowerShell.Commands.GetContentCommand

WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready
Get-Content : Cannot find path 'E:\genesis-system\data\memory_digestion_last_run.log' because it does not exist.
At line:1 char:1
+ Get-Content 'E:\genesis-system\data\memory_digestion_last_run.log' -T ...
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    + CategoryInfo          : ObjectNotFound: (E:\genesis-syst...on_last_run.log:String) [Get-Content], ItemNotFoundEx
   ception
    + FullyQualifiedErrorId : PathNotFound,Microsoft.PowerShell.Commands.GetContentCommand


Output:
WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready


python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event session_stop


Failed with non-blocking status code: 'python3' is not recognized as an internal or external command,
operable program or batch file.


<task-notification>
<task-id>bd46083</task-id>
<output-file>C:\Users\P3\AppData\Local\Temp\claude\E--genesis-system--claude-worktrees-eloquent-austin\tasks\bd46083.output</output-file>
<status>completed</status>
<summary>Background command "cd /e/genesis-system && ls -la .claude/hooks/ | grep -E "memory|session|rlm|context" && echo "---" && find . -name "*preference*" -o -name "*mentor*" 2>/dev/null | head -10" completed (exit code 0)</summary>
</task-notification>
Read the output file to retrieve the result: C:\Users\P3\AppData\Local\Temp\claude\E--genesis-system--claude-worktrees-eloquent-austin\tasks\bd46083.output


The subagent that ran earlier must have triggered this background task. Let me read the output.


     1→-rwxr-xr-x 1 P3 197121  2920 Feb 23 03:27 boot_context_inject.py
     2→-rwxr-xr-x 1 P3 197121  2336 Feb 18 07:26 context_70pct_flag.sh
     3→-rwxr-xr-x 1 P3 197121  3415 Feb 14 19:13 context_tracker.py
     4→-rwxr-xr-x 1 P3 197121  5971 Feb 23 08:21 memory_context_inject.py
     5→-rwxr-xr-x 1 P3 197121  8967 Feb 15 12:32 session_heartbeat.py
     6→-rwxr-xr-x 1 P3 197121 14915 Feb 23 03:36 session_recovery_start.py
     7→-rwxr-xr-x 1 P3 197121  5714 Feb 14 18:15 session_sync.py
     8→-rwxr-xr-x 1 P3 197121  5679 Feb 22 13:09 sunaiva_session_capture.py
     9→-rwxr-xr-x 1 P3 197121  3806 Feb 23 03:36 sunaiva_session_inject.py
    10→---
    11→./.cache-redirect/antigravity/extensions/redhat.java-1.52.0-win32-x64/server/plugins/org.eclipse.equinox.preferences_3.12.100.v20251111-0704.jar
    12→./.claude/worktrees/charming-dirac/AIVA/autonomy/mentorship_extractor.py
    13→./.claude/worktrees/charming-dirac/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
    14→./.claude/worktrees/dreamy-dirac/AIVA/autonomy/mentorship_extractor.py
    15→./.claude/worktrees/dreamy-dirac/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
    16→./.claude/worktrees/eloquent-austin/AIVA/autonomy/mentorship_extractor.py
    17→./.claude/worktrees/eloquent-austin/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
    18→./.claude/worktrees/intelligent-yalow/AIVA/autonomy/mentorship_extractor.py
    19→./.claude/worktrees/intelligent-yalow/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
    20→./.venv/lib/python3.12/site-packages/yt_dlp/extractor/elementorembed.py
    21→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

-rwxr-xr-x 1 P3 197121  2920 Feb 23 03:27 boot_context_inject.py
-rwxr-xr-x 1 P3 197121  2336 Feb 18 07:26 context_70pct_flag.sh
-rwxr-xr-x 1 P3 197121  3415 Feb 14 19:13 context_tracker.py
-rwxr-xr-x 1 P3 197121  5971 Feb 23 08:21 memory_context_inject.py
-rwxr-xr-x 1 P3 197121  8967 Feb 15 12:32 session_heartbeat.py
-rwxr-xr-x 1 P3 197121 14915 Feb 23 03:36 session_recovery_start.py
-rwxr-xr-x 1 P3 197121  5714 Feb 14 18:15 session_sync.py
-rwxr-xr-x 1 P3 197121  5679 Feb 22 13:09 sunaiva_session_capture.py
-rwxr-xr-x 1 P3 197121  3806 Feb 23 03:36 sunaiva_session_inject.py
---
./.cache-redirect/antigravity/extensions/redhat.java-1.52.0-win32-x64/server/plugins/org.eclipse.equinox.preferences_3.12.100.v20251111-0704.jar
./.claude/worktrees/charming-dirac/AIVA/autonomy/mentorship_extractor.py
./.claude/worktrees/charming-dirac/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
./.claude/worktrees/dreamy-dirac/AIVA/autonomy/mentorship_extractor.py
./.claude/worktrees/dreamy-dirac/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
./.claude/worktrees/eloquent-austin/AIVA/autonomy/mentorship_extractor.py
./.claude/worktrees/eloquent-austin/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
./.claude/worktrees/intelligent-yalow/AIVA/autonomy/mentorship_extractor.py
./.claude/worktrees/intelligent-yalow/AIVA/queen_outputs/rlm/rlm_01_preference_learning.py
./.venv/lib/python3.12/site-packages/yt_dlp/extractor/elementorembed.py


This is relevant additional data. A few things this confirms and reveals:

**Confirms:**
- `memory_context_inject.py` is the newest hook (Feb 23), actively maintained
- `sunaiva_session_capture.py` and `sunaiva_session_inject.py` are the cross-session memory hooks (Grov-style)
- Session hooks chain is intact on disk

**New finding — critical:**
- `mentorship_extractor.py` and `rlm_01_preference_learning.py` exist in **AIVA's autonomy/queen_outputs directories** — but these are in the worktrees (`charming-dirac`, `dreamy-dirac`, `eloquent-austin`, `intelligent-yalow`), **not** in the main `core/` pipeline
- This is why mentor pairs are 0 — `rlm_bloodstream_pipeline.py` Step 5 tries to generate preference pairs, but it's looking at an empty `aiva_interactions` table. The actual preference learning code (`rlm_01_preference_learning.py`) lives in AIVA's RL modules directory (the other RLM — Reinforcement Learning from Mentorship), **not** wired to the Recursive Language Model bloodstream pipeline at all

This confirms the naming disambiguation issue hardwired in GLOBAL_GENESIS_RULES (Rule 15) is more than just semantic — the two systems are **architecturally disconnected**, and the bloodstream pipeline's Step 5 is trying to bridge them but the bridge to `aiva_interactions` is empty.

**This doesn't change the overall score** — but adds precision to Fix #1: the mentor pair gap isn't a bug, it's an integration that was never completed. AIVA's preference learning data (`rlm_01_preference_learning.py`) has never been routed into the bloodstream's `pl_preference_pairs` table.


wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/boot_context_inject.py


bash -c 'cd /mnt/e/genesis-system && python3 core/war_room_dashboard.py --session-banner'


lets take a step back and ultrathink to consider this persistent context problem to be the number one to solve before all else. agentic autonomous revenue creation simply isnt possible from such a disjointed fragmented foundation. it seems like we are good at building but not at connecting. lets ask a series of targeted questions to google deep mind via gemini deep think mode prompts now. we need to assess our current architecture and see if we glue or we snip and rebuild. lets deeply research and understand the framework so we can then understand better how to incorporate it into Genesis. let ask meta-questions engineered to elicit higher level thinking for game changing capability breakthroughs and synergies.


This is the right call. The assessment I just did confirms it — Genesis has 1,000 puzzle pieces but the picture isn't assembled. Before we touch Gemini, let me frame the exact meta-questions needed.


TodoWrite

Frame and dispatch deep meta-questions to Gemini Deep Think on persistent context architecture

Framing meta-questions for Gemini Deep Think session


Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

Frame and dispatch deep meta-questions to Gemini Deep Think on persistent context architecture

Framing meta-questions for Gemini Deep Think session


Let me first rapidly inventory what we actually have vs. what's broken, so the Gemini questions are surgical, not generic.


Bash

powershell -Command "Get-Content 'E:\genesis-system\data\bloodstream_last_run.log' | Select-Object -Last 60"

Read last bloodstream pipeline run log


WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready
[2026-02-20 12:23:52] Pipeline run complete
  Source: manual
  Dry run: False
  KG files scanned: 79
  Entries found: 885
  New entries loaded: 885
  Qdrant embeddings: 0
  Redis cache items: 50
  Digestion: completed
  Mentor pairs generated: 0
  Duration: 443.9s

[2026-02-24 04:07:52] Pipeline run complete
  Source: deep_think_ingest
  Dry run: False
  KG files scanned: 133
  Entries found: 15720
  New entries loaded: 14835
  Qdrant embeddings: 0
  Redis cache items: 50
  Digestion: error
  Mentor pairs generated: 0
  Duration: 7328.3s
  Errors (1):
    - Step 4 MemoryDigestion error: cannot import name 'MemoryDigestion' from 'core.memory_digestion' (/mnt/e/genesis-system/core/memory_digestion.py)

[2026-02-24 07:00:20] Pipeline run complete
  Source: kb_expansion
  Dry run: False
  KG files scanned: 134
  Entries found: 20234
  New entries loaded: 4514
  Qdrant embeddings: 4514
  Redis cache items: 50
  Digestion: completed
  Mentor pairs generated: 0
  Duration: 7707.3s


powershell -Command "Get-Content 'E:\genesis-system\data\session_ingest_processed.txt' | Measure-Object -Line | Select-Object Lines; Get-Content 'E:\genesis-system\data\context_state\injected_context.md' -ErrorAction SilentlyContinue | Select-Object -First 40"

Check session ingest state and injected context


Error: Exit code 1
WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready

Lines
-----
 1295


File does not exist.


powershell -Command "Get-ChildItem 'E:\genesis-system\data\context_state\' | Select-Object Name, LastWriteTime | Sort-Object LastWriteTime -Descending"

Check context_state directory contents


WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready

Name                        LastWriteTime
----                        -------------
last_c_cleanup.json         24/02/2026 5:23:49 PM
usage_tracker_counter.json  24/02/2026 5:23:24 PM
heartbeat_counter.json      24/02/2026 5:23:24 PM
capability_manifest.json    24/02/2026 5:19:24 PM
compaction_log.jsonl        24/02/2026 5:17:26 PM
thinking_monitor.json       24/02/2026 9:45:05 AM
current.json                23/02/2026 3:25:31 AM
BOOT_CONTEXT.md             23/02/2026 3:14:52 AM
auto_respawn_triggers.jsonl 22/02/2026 6:50:45 AM
claude_api_usage.json       19/02/2026 5:50:15 AM
respawn_log.jsonl           17/02/2026 3:55:41 AM
respawn_output.log          17/02/2026 3:23:13 AM
ctm_triggers.jsonl          14/02/2026 7:52:17 PM


     1→{
     2→  "timestamp": "2026-02-24T07:19:24.885527+00:00",
     3→  "model": "claude-opus-4-6",
     4→  "context_window": "200K",
     5→  "output_limit": "128K",
     6→  "thinking_mode": "adaptive",
     7→  "mcps_connected": [
     8→    "brave-search",
     9→    "genesis-core",
    10→    "genesis-full",
    11→    "genesis-mem0",
    12→    "genesis-v2",
    13→    "genesis-voice-bridge",
    14→    "ghl",
    15→    "github",
    16→    "graphiti-memory",
    17→    "instantly-outreach",
    18→    "patent-os",
    19→    "playwright",
    20→    "sequential-thinking",
    21→    "sunaiva-memory",
    22→    "youtube-transcript"
    23→  ],
    24→  "beta_features": [
    25→    "agent-teams-experimental",
    26→    "computer-use-2025-01-24"
    27→  ],
    28→  "env_vars": {
    29→    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1",
    30→    "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE": "85"
    31→  },
    32→  "capability_score": 100
    33→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

brave-search genesis-core genesis-full genesis-mem0 genesis-v2 genesis-voice-bridge ghl github graphiti-memory instantly-outreach patent-os playwright sequential-thinking sunaiva-memory youtube-transcript


Good. Now I have the complete picture. Let me formulate the meta-questions and open Gemini.


### Ran Playwright code
```js
await page.goto('https://gemini.google.com');
```
### Page
- Page URL: https://gemini.google.com/app
- Page Title: Google Gemini
- Console: 2 errors, 1 warnings
### Snapshot
```yaml
- generic [ref=e1]:
  - link "Sign in" [ref=e6] [cursor=pointer]:
    - /url: https://accounts.google.com/ServiceLogin?passive=1209600&continue=https://gemini.google.com/&followup=https://gemini.google.com/&ec=GAZAkgU
  - main [ref=e8]:
    - generic:
      - button "Main menu" [ref=e10] [cursor=pointer]:
        - img [ref=e11]: menu
      - link "New chat" [ref=e15] [cursor=pointer]:
        - /url: /app
        - generic [ref=e17]: Gemini
    - generic [ref=e29]:
      - heading "Conversation with Gemini" [level=1] [ref=e31]
      - generic [ref=e44]:
        - group [ref=e45]:
          - generic [ref=e48]:
            - textbox "Enter a prompt for Gemini" [active] [ref=e53]:
              - text: Ask Gemini 3
              - paragraph [ref=e54]
            - generic [ref=e56]:
              - group [ref=e59]:
                - button "Open mode picker" [ref=e60]:
                  - generic [ref=e62]:
                    - generic [ref=e63]: Fast
                    - img [ref=e64]: keyboard_arrow_down
              - generic [ref=e67]:
                - button "Microphone" [ref=e70] [cursor=pointer]:
                  - img [ref=e72]: mic
                - text: send
        - paragraph [ref=e77]:
          - link "Google Terms Opens in a new window" [ref=e78] [cursor=pointer]:
            - /url: https://policies.google.com/terms
            - text: Google Terms
            - generic [ref=e79]: Opens in a new window
          - text: and the
          - link "Google Privacy Policy Opens in a new window" [ref=e80] [cursor=pointer]:
            - /url: https://policies.google.com/privacy
            - text: Google Privacy Policy
            - generic [ref=e81]: Opens in a new window
          - text: apply. Gemini is AI and can make mistakes.
```
### Events
- New console entries: .playwright-mcp\console-2026-02-24T08-03-48-562Z.log#L1-L3
- [INFO] Loading the script 'https://www.googletagma...action has been taken. @ https://gemini.google.com/:0
- [ERROR] Connecting to 'https://stats.g.doubleclick...tag/destination?id=G-BF8Q35BMLM&cx=c&gtm=4e62j0h1:218
- [ERROR] Loading the image 'https://www.google.com....n has been blocked. @ https://gemini.google.com/app:0
- [WARNING] No ID or name found in config. @ https:/...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:984


Not logged in. Per Rule 12 — collaborative browser handoff. I need you to log into `kinan@agileadapt.com` in the browser window that just opened, then I'll take over.

Actually, let me check if there's an existing authenticated session first via the Claude in Chrome MCP which may have an active tab.


⚠ **Claude in Chrome Extension Disconnected**

The connection to Claude in Chrome was lost.

**To reconnect:**
1. Login to the same account in Claude Desktop and Claude in Chrome
2. If that doesn't work, restart Chrome completely (close all windows)
3. If that doesn't work, also restart Claude Desktop


I'll log into Gemini in the browser. Please log into kinan@agileadapt.com in the Playwright browser window that opened, then let me know when you're in and I'll take over with the deep think prompts.

While you do that, let me finish formulating the exact meta-questions so they're ready to fire the moment you're authenticated.

---

## The Meta-Questions I'm Engineering

Here's the full framing of what we're bringing to Gemini Deep Think. These are deliberately engineered to be *above* "how do I fix X" — they're asking for architectural paradigm shifts.

---

### The Context (what I'll inject)

Genesis has:
- **14+ MCPs** connected (Qdrant, PostgreSQL, Redis, Playwright, GHL, Telnyx, n8n, Sunaiva memory, etc.)
- **106 KG entity files + 54 axiom files** on disk
- **20,000+ entries** loaded into PostgreSQL `bloodstream_knowledge`
- **4,514 Qdrant vector embeddings**
- **Session inject hooks** (SessionStart fires 5 scripts injecting memory into Claude's context)
- **Bloodstream pipeline** runs 3x total — manually triggered, not autonomous
- **Execution layer** (Claude orchestrates → Gemini swarm executes) — but RLM is NOT wired into it
- **0 mentor preference pairs** ever generated (the learning feedback loop has never fired)
- **Fragmentation**: write-only memory, disconnected execution, no read-back loop

The core pathology: **Genesis is excellent at building components and terrible at connecting them into a living system.**

---

### The 7 Meta-Questions for Gemini Deep Think

**Q1 — The Architecture Paradigm Question:**
> "I am building an autonomous AI agent system ('Genesis') that runs Claude Opus as orchestrator with a Gemini execution swarm. The system has rich memory infrastructure: PostgreSQL (20K+ knowledge entries), Qdrant vector DB (4,500 embeddings), Redis hot cache, a KG with 160 files, and session hooks that inject context at session start. Yet despite all this infrastructure, the system has zero persistent learning — each session starts essentially fresh, the memory write pipeline runs manually 3x total, and the execution layer never queries the memory system. The components exist but are architecturally disconnected. What is the fundamental paradigm that separates an AI system that *has* memory from one that *uses* memory? What architectural pattern creates genuine continuous learning in agentic systems, and what are the 3-5 non-negotiable integration points that must exist for memory to become genuinely operational rather than decorative?"

**Q2 — The Feedback Loop Question:**
> "In autonomous agentic AI systems, what is the minimal viable feedback loop that enables genuine self-improvement? Not theoretical — what are the specific data flows, trigger mechanisms, and storage patterns that world-class AI agent systems (like those at Google DeepMind, character.ai, or Cognition's Devin) use to ensure that every interaction makes the system measurably better at the next interaction? I want to understand the exact anatomy of a working feedback loop at the systems architecture level."

**Q3 — The Glue vs. Rebuild Question:**
> "A complex AI agent system has accumulated 18 months of components: memory stores, execution pipelines, KG files, hook scripts, MCP integrations. Many components are real and working in isolation but are not wired together. The team is excellent at building but has a systematic blindspot around integration. Given this pattern, what is the architectural decision framework for choosing between: (A) building glue/connective tissue to integrate existing components, vs (B) replacing fragmented components with a unified framework that has integration as a first-class design principle? What signals indicate which path to take, and what are the failure modes of each?"

**Q4 — The Agentic Revenue Question:**
> "What does a truly autonomous AI agent revenue system require at the architectural level — not the business model, but the technical substrate — to move from 'human assigns tasks' to 'system identifies and executes revenue-generating actions autonomously'? What is the critical path of capabilities that must exist and be connected? Specifically: what does the memory/context layer need to look like, what does the action execution layer need to look like, and what is the coordination mechanism between them?"

**Q5 — The Context Persistence Question:**
> "Claude Code's context window resets every session. We have 5 SessionStart hook scripts that inject memory from PostgreSQL and KG files into the system prompt. But the injected context is static snapshots, not live queries. The agent effectively re-reads the same summaries each session rather than building on prior sessions. What are the architectural patterns used by the most sophisticated long-running AI agent systems (Devin, SWE-agent, AutoGPT's successors, etc.) to achieve genuine cross-session continuity — not just context injection, but actual progressive capability accumulation? What would a 'zero amnesia' architecture look like?"

**Q6 — The Multi-Agent Coherence Question:**
> "Genesis runs Claude as orchestrator and dispatches tasks to a Gemini swarm (10-20 parallel agents). The agents share a PostgreSQL database and Redis cache but have no shared working memory during execution — they write outputs to files and tables that are read after the fact. What architectural pattern would give the swarm agents genuine real-time coherence — the ability to build on each other's in-progress work rather than working in isolated parallel? What are the trade-offs between blackboard architecture, shared context injection, message-passing, and event-driven patterns for multi-agent coordination?"

**Q7 — The Meta-System Question:**
> "What would it look like for an AI agent system to be genuinely self-improving at the architectural level — not just learning within its domain, but improving its own memory systems, routing logic, prompt quality, and agent coordination? What are the theoretical requirements for a system to be 'architecturally self-modifying' and what are the safety constraints that must exist? Is this achievable with current LLM technology or does it require capabilities not yet available?"

---

Once you're logged in, I'll fire these one at a time in Deep Think mode — each one designed to pull out a different dimension of the architecture problem. The goal is to synthesize the answers into a concrete **Genesis Persistent Context Architecture v2** design that either integrates what exists or tells us what to kill.

**Ready when you are — just say "go" after you've logged in.**


Output:
auto_respawn_triggers.jsonl 22/02/2026 6:50:45 AM
claude_api_usage.json       19/02/2026 5:50:15 AM
respawn_log.jsonl           17/02/2026 3:55:41 AM
respawn_output.log          17/02/2026 3:23:13 AM
ctm_triggers.jsonl          14/02/2026 7:52:17 PM

WARNING: Genesis additions not found at e:\genesis-system\setup\antigravity_profile_additions.ps1
dYs? Antigravity Terminal Ready
Name                        LastWriteTime
----                        -------------
last_c_cleanup.json         24/02/2026 5:23:49 PM
usage_tracker_counter.json  24/02/2026 5:23:24 PM
heartbeat_counter.json      24/02/2026 5:23:24 PM
capability_manifest.json    24/02/2026 5:19:24 PM
compaction_log.jsonl        24/02/2026 5:17:26 PM
thinking_monitor.json       24/02/2026 9:45:05 AM
current.json                23/02/2026 3:25:31 AM
BOOT_CONTEXT.md             23/02/2026 3:14:52 AM
auto_respawn_triggers.jsonl 22/02/2026 6:50:45 AM
claude_api_usage.json       19/02/2026 5:50:15 AM
respawn_log.jsonl           17/02/2026 3:55:41 AM
respawn_output.log          17/02/2026 3:23:13 AM
ctm_triggers.jsonl          14/02/2026 7:52:17 PM


open


### Page
- Page URL: https://gemini.google.com/app
- Page Title: Google Gemini
- Console: 2 errors, 6 warnings
### Snapshot
```yaml
- generic [active] [ref=e1]:
  - 'button "WORK, Google Account: Kinan Lemberg (kinan@agileadapt.com)" [ref=e8] [cursor=pointer]':
    - img [ref=e10]
  - main [ref=e12]:
    - generic:
      - button "Main menu" [ref=e14] [cursor=pointer]:
        - img [ref=e15]: menu
      - link "New chat" [ref=e19] [cursor=pointer]:
        - /url: /app
        - generic [ref=e21]: Gemini
    - generic [ref=e26]:
      - generic [ref=e27]:
        - button "ULTRA" [disabled]:
          - generic: ULTRA
      - generic [ref=e31]: WORK
    - generic [ref=e33]:
      - navigation [ref=e34]:
        - generic [ref=e38]:
          - group [ref=e39]:
            - link "New chat" [ref=e41] [cursor=pointer]:
              - /url: /app
              - img [ref=e44]: edit_square
          - group [ref=e47]:
            - button "Settings & help" [ref=e49] [cursor=pointer]:
              - img [ref=e52]: settings_2
      - generic [ref=e61]:
        - generic [ref=e62]:
          - heading "Conversation with Gemini" [level=1] [ref=e63]
          - generic [ref=e65]:
            - generic [ref=e68]:
              - generic [ref=e69]:
                - heading "user greeting" [level=2] [ref=e74]:
                  - generic [ref=e75]: Hi Kinan
                - heading "Where should we start?" [level=1] [ref=e82]
              - generic [ref=e90]:
                - button "🍌 Create image, button, tap to use tool" [ref=e92] [cursor=pointer]:
                  - generic [ref=e93]: 🍌 Create image
                - button "🎸 Create music, button, tap to use tool" [ref=e95] [cursor=pointer]:
                  - generic [ref=e96]: 🎸 Create music
                - button "Write anything, button, tap to use tool" [ref=e98] [cursor=pointer]:
                  - generic [ref=e99]: Write anything
                - button "Help me learn, button, tap to use tool" [ref=e101] [cursor=pointer]:
                  - generic [ref=e102]: Help me learn
                - button "Boost my day, button, tap to use tool" [ref=e104] [cursor=pointer]:
                  - generic [ref=e105]: Boost my day
            - generic [ref=e107]:
              - text: You're using Deep Think (experimental), our most advanced reasoning feature.
              - link "Learn more Opens in a new window" [ref=e108] [cursor=pointer]:
                - /url: https://support.google.com/gemini?p=deep_think
                - text: Learn more
                - generic [ref=e109]: Opens in a new window
        - generic [ref=e110]:
          - group [ref=e111]:
            - generic [ref=e114]:
              - textbox "Enter a prompt for Gemini" [ref=e119]:
                - text: Ask a complex question
                - paragraph [ref=e120]
                - text: encrypted
              - generic [ref=e121]:
                - button "Open upload file menu" [ref=e126] [cursor=pointer]:
                  - img [ref=e127]: add_2
                - generic [ref=e132]:
                  - button "Tools" [ref=e133] [cursor=pointer]:
                    - img [ref=e135]: page_info
                  - button "Deselect Deep Think" [ref=e138]:
                    - img [ref=e139]: mindfulness
                    - generic [ref=e140]: Deep Think
                    - img [ref=e141]: close
              - generic [ref=e144]:
                - button "Open mode picker" [ref=e147]:
                  - generic [ref=e149]:
                    - generic [ref=e150]: Pro
                    - img [ref=e151]: keyboard_arrow_down
                - generic [ref=e154]:
                  - button "Microphone" [ref=e157] [cursor=pointer]:
                    - img [ref=e159]: mic
                  - text: send
          - paragraph [ref=e164]
```
### Events
- New console entries: .playwright-mcp\console-2026-02-24T08-03-48-562Z.log#L4-L10
- [VERBOSE] [DOM] Password field is not contained in...09600&flowName=GlifWebSignIn&flowEntry=ServiceLogin:0
- [LOG] %c%s color: red; background: yellow; font-si...AOaEmlE1tbdx1o0-iwdpEJJR89speVxs2A/dti=1/m=_b,_tp:462
- [LOG] %c%s font-size: 18px; Using this console may...AOaEmlE1tbdx1o0-iwdpEJJR89speVxs2A/dti=1/m=_b,_tp:462
- [VERBOSE] [DOM] Password field is not contained in...4k9LSqnWDAKt-tTvHak09CFxpLRAlB-hRrFBgH9wSw&pstMsg=1:0
- [INFO] Loading the script 'https://www.googletagma...action has been taken. @ https://gemini.google.com/:0
- [WARNING] No ID or name found in config. @ https:/...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:987
- [WARNING] No ID or name found in config. @ https:/...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:987
- [WARNING] No ID or name found in config. @ https:/...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:987
- [WARNING] No ID or name found in config. @ https:/...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:987
- [WARNING] Blocked aria-hidden on an element becaus...4100414-0#app-root> @ https://gemini.google.com/app:0


✅ **kinan@agileadapt.com logged in, ULTRA + Deep Think active.** Taking over now.

Firing Q1 first — the foundational architecture paradigm question.


### Ran Playwright code
```js
await page.getByRole('textbox', { name: 'Enter a prompt for Gemini' }).click();
```
### Page
- Page URL: https://gemini.google.com/app
- Page Title: Google Gemini
- Console: 2 errors, 6 warnings
### Snapshot
```yaml
- <changed> generic [ref=e1]:
  - ref=e8 [unchanged]
  - main [ref=e12]:
    - generic:
      - ref=e14 [unchanged]
      - ref=e19 [unchanged]
    - ref=e26 [unchanged]
    - generic [ref=e33]:
      - ref=e34 [unchanged]
      - generic [ref=e61]:
        - ref=e62 [unchanged]
        - generic [ref=e110]:
          - group [ref=e111]:
            - generic [ref=e114]:
              - textbox "Enter a prompt for Gemini" [active] [ref=e119]:
                - text: Ask a complex question
                - ref=e120 [unchanged]
                - text: encrypted
              - ref=e121 [unchanged]
              - ref=e144 [unchanged]
          - ref=e164 [unchanged]
```


I am building an autonomous AI agent system called Genesis that runs Claude Opus as orchestrator with a Gemini execution swarm. The system has rich memory infrastructure: PostgreSQL with 20,000+ knowledge entries, Qdrant vector DB with 4,500 embeddings, Redis hot cache, a Knowledge Graph with 160 files (entities + axioms), and SessionStart hook scripts that inject context snapshots into Claude's system prompt at session start. Yet despite all this infrastructure, the system has zero persistent learning in practice — each session starts essentially fresh, the memory write pipeline has run only 3 times manually, the execution layer never queries the memory system before dispatching tasks, and the feedback loop (mentor preference pairs) has generated 0 entries across all runs. The components exist but are architecturally disconnected.

Q1 — THE ARCHITECTURE PARADIGM:

What is the fundamental paradigm that separates an AI agent system that *has* memory from one that genuinely *uses* memory? What architectural pattern creates genuine continuous learning in agentic systems, and what are the 3-5 non-negotiable integration points that must exist for memory to become operational rather than decorative?

Please think deeply about this. I want to understand the difference at a systems architecture level — not "add a vector DB" but the actual data flow contracts, trigger mechanisms, and feedback loops that make memory live. Use examples from world-class systems (Devin, SWE-agent, MemGPT, character.ai, Google's own agent research) where relevant. This will inform a fundamental architectural decision about whether to glue our existing components or rebuild with integration as a first-class design principle.


### Ran Playwright code
```js
await page.getByRole('textbox', { name: 'Enter a prompt for Gemini' }).fill('I am building an autonomous AI agent system called Genesis that runs Claude Opus as orchestrator with a Gemini execution swarm. The system has rich memory infrastructure: PostgreSQL with 20,000+ knowledge entries, Qdrant vector DB with 4,500 embeddings, Redis hot cache, a Knowledge Graph with 160 files (entities + axioms), and SessionStart hook scripts that inject context snapshots into Claude\'s system prompt at session start. Yet despite all this infrastructure, the system has zero persistent learning in practice — each session starts essentially fresh, the memory write pipeline has run only 3 times manually, the execution layer never queries the memory system before dispatching tasks, and the feedback loop (mentor preference pairs) has generated 0 entries across all runs. The components exist but are architecturally disconnected. \n\nQ1 — THE ARCHITECTURE PARADIGM:\n\nWhat is the fundamental paradigm that separates an AI agent system that *has* memory from one that genuinely *uses* memory? What architectural pattern creates genuine continuous learning in agentic systems, and what are the 3-5 non-negotiable integration points that must exist for memory to become operational rather than decorative? \n\nPlease think deeply about this. I want to understand the difference at a systems architecture level — not "add a vector DB" but the actual data flow contracts, trigger mechanisms, and feedback loops that make memory live. Use examples from world-class systems (Devin, SWE-agent, MemGPT, character.ai, Google\'s own agent research) where relevant. This will inform a fundamental architectural decision about whether to glue our existing components or rebuild with integration as a first-class design principle.');
```


### Ran Playwright code
```js
// Press Enter
await page.keyboard.press('Enter');
```
### Page
- Page URL: https://gemini.google.com/app
- Page Title: Google Gemini
- Console: 7 errors, 6 warnings
### Snapshot
```yaml
- <changed> generic [ref=e62]:
  - ref=e63 [unchanged]
  - generic [ref=e183]:
    - generic [ref=e188]:
      - button "Copy prompt" [ref=e190] [cursor=pointer]:
        - img [ref=e191]: content_copy
      - generic [ref=e194]:
        - button "Edit" [disabled]:
          - img: edit
      - generic [ref=e196]:
        - heading [level=2] [ref=e197]:
          - generic [ref=e198]: You said
          - paragraph [ref=e199]: "I am building an autonomous AI agent system called Genesis that runs Claude Opus as orchestrator with a Gemini execution swarm. The system has rich memory infrastructure: PostgreSQL with 20,000+ knowledge entries, Qdrant vector DB with 4,500 embeddings, Redis hot cache, a Knowledge Graph with 160 files (entities + axioms), and SessionStart hook scripts that inject context snapshots into Claude's system prompt at session start. Yet despite all this infrastructure, the system has zero persistent learning in practice — each session starts essentially fresh, the memory write pipeline has run only 3 times manually, the execution layer never queries the memory system before dispatching tasks, and the feedback loop (mentor preference pairs) has generated 0 entries across all runs. The components exist but are architecturally disconnected."
          - paragraph [ref=e200]
          - paragraph [ref=e201]: "Q1 — THE ARCHITECTURE PARADIGM:"
          - paragraph [ref=e202]
          - paragraph [ref=e203]: What is the fundamental paradigm that separates an AI agent system that *has* memory from one that genuinely *uses* memory? What architectural pattern creates genuine continuous learning in agentic systems, and what are the 3-5 non-negotiable integration points that must exist for memory to become operational rather than decorative?
          - paragraph [ref=e204]
          - paragraph [ref=e205]: Please think deeply about this. I want to understand the difference at a systems architecture level — not "add a vector DB" but the actual data flow contracts, trigger mechanisms, and feedback loops that make memory live. Use examples from world-class systems (Devin, SWE-agent, MemGPT, character.ai, Google's own agent research) where relevant. This will inform a fundamental architectural decision about whether to glue our existing components or rebuild with integration as a first-class design principle.
        - button "Expand" [ref=e206] [cursor=pointer]:
          - img [ref=e207]: expand_more
    - generic [ref=e216]:
      - generic [ref=e218]:
        - generic:
          - generic:
            - img
      - generic:
        - img
- <changed> generic [ref=e110]:
  - group [ref=e111]:
    - generic [ref=e114]:
      - textbox "Enter a prompt for Gemini" [active] [ref=e119]:
        - text: Ask a complex question
        - paragraph [ref=e220]
        - text: encrypted
      - ref=e121 [unchanged]
      - generic [ref=e144]:
        - ref=e147 [unchanged]
        - generic [ref=e154]:
          - text: mic
          - button "Stop response" [ref=e221] [cursor=pointer]:
            - text: send
            - img [ref=e223]: stop
  - paragraph [ref=e224]:
    - text: Your AgileAdapt chats aren't used to improve our models. Gemini is AI and can make mistakes.
    - link "Your privacy and Gemini Opens in a new window" [ref=e225] [cursor=pointer]:
      - /url: https://support.google.com/gemini/answer/14620100
      - text: Your privacy and Gemini
      - generic [ref=e226]: Opens in a new window
```
### Events
- New console entries: .playwright-mcp\console-2026-02-24T08-03-48-562Z.log#L11-L15
- [ERROR] Loading the image 'https://www.google.com....n has been blocked. @ https://gemini.google.com/app:0
- [ERROR] Connecting to 'https://www.googleadservice...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:529
- [ERROR] Fetch API cannot load https://www.googlead...%3BBardChatUi.mF8WCutAWy8.loadWasmSipCoca.O%3A%3B:529
- [ERROR] Attestation check for Attribution Reportin...alytics.com failed. @ https://gemini.google.com/app:0
- [ERROR] Connecting to 'https://www.google.com.au/p...n has been blocked. @ https://gemini.google.com/app:0


Q1 is processing in Deep Think mode. Now I'll wait for the response — Deep Think takes 30-90 seconds.


non-negotiable


### Result
Waited for non-negotiable
### Ran Playwright code
```js
await new Promise(f => setTimeout(f, 90 * 1000));
await page.getByText("non-negotiable").first().waitFor({ state: 'visible' });
```
### Open tabs
- 0: (current) [Google Gemini](https://gemini.google.com/app/960b95c7445299a9)
- 1: [Settings - Appearance](chrome://settings/appearance)
### Page
- Page URL: https://gemini.google.com/app/960b95c7445299a9
- Page Title: Google Gemini
- Console: 7 errors, 6 warnings
### Snapshot
```yaml
- <changed> generic [active] [ref=e1]:
  - ref=e8 [unchanged]
  - main [ref=e12]:
    - generic:
      - ref=e14 [unchanged]
      - ref=e19 [unchanged]
    - generic [ref=e23]:
      - button "Open menu for conversation actions." [ref=e230] [cursor=pointer]:
        - generic [ref=e231]: AI Agent Memory and Learning Architecture
        - img [ref=e232]: keyboard_arrow_down
      - ref=e26 [unchanged]
    - generic [ref=e33]:
      - ref=e34 [unchanged]
      - generic [ref=e61]:
        - generic [ref=e62]:
          - ref=e63 [unchanged]
          - generic [ref=e233]:
            - generic [ref=e238]:
              - button "Copy prompt" [ref=e240] [cursor=pointer]:
                - img [ref=e241]: content_copy
              - generic [ref=e244]:
                - button "Edit" [disabled]:
                  - img: edit
              - generic [ref=e246]:
                - heading [level=2] [ref=e247]:
                  - generic [ref=e248]: You said
                  - paragraph [ref=e249]: "I am building an autonomous AI agent system called Genesis that runs Claude Opus as orchestrator with a Gemini execution swarm. The system has rich memory infrastructure: PostgreSQL with 20,000+ knowledge entries, Qdrant vector DB with 4,500 embeddings, Redis hot cache, a Knowledge Graph with 160 files (entities + axioms), and SessionStart hook scripts that inject context snapshots into Claude's system prompt at session start. Yet despite all this infrastructure, the system has zero persistent learning in practice — each session starts essentially fresh, the memory write pipeline has run only 3 times manually, the execution layer never queries the memory system before dispatching tasks, and the feedback loop (mentor preference pairs) has generated 0 entries across all runs. The components exist but are architecturally disconnected."
                  - paragraph [ref=e250]
                  - paragraph [ref=e251]: "Q1 — THE ARCHITECTURE PARADIGM:"
                  - paragraph [ref=e252]
                  - paragraph [ref=e253]: What is the fundamental paradigm that separates an AI agent system that *has* memory from one that genuinely *uses* memory? What architectural pattern creates genuine continuous learning in agentic systems, and what are the 3-5 non-negotiable integration points that must exist for memory to become operational rather than decorative?
                  - paragraph [ref=e254]
                  - paragraph [ref=e255]: Please think deeply about this. I want to understand the difference at a systems architecture level — not "add a vector DB" but the actual data flow contracts, trigger mechanisms, and feedback loops that make memory live. Use examples from world-class systems (Devin, SWE-agent, MemGPT, character.ai, Google's own agent research) where relevant. This will inform a fundamental architectural decision about whether to glue our existing components or rebuild with integration as a first-class design principle.
                - button "Expand" [ref=e256] [cursor=pointer]:
                  - img [ref=e257]: expand_more
            - generic [ref=e264]:
              - generic [ref=e269]:
                - generic:
                  - generic:
                    - img
              - generic [ref=e271]:
                - heading "Gemini said" [level=2] [ref=e272]
                - paragraph [ref=e277]:
                  - text: I'm on it. Responses with Deep Think can take some time, so check back in a bit.
                  - generic [ref=e280]:
                    - generic [ref=e281]:
                      - img [ref=e282]: mindfulness
                      - img [ref=e285]
                    - generic [ref=e290]:
                      - generic [ref=e291]: Generating your response…
                      - generic [ref=e292]: Check back later
        - generic [ref=e110]:
          - group [ref=e111]:
            - generic [ref=e114]:
              - textbox "Enter a prompt for Gemini" [ref=e119]:
                - text: Ask a complex question
                - ref=e220 [unchanged]
                - text: encrypted
              - ref=e121 [unchanged]
              - generic [ref=e144]:
                - ref=e147 [unchanged]
                - generic [ref=e154]:
                  - text: mic
                  - button "Stop response" [ref=e221] [cursor=pointer]:
                    - text: send
                    - img [ref=e294]: stop
          - ref=e224 [unchanged]
```


Deep Think is still generating — "Check back later" means it's doing extended reasoning. This is normal for Deep Think mode on hard questions. Let me wait longer.


Error: ### Error
TimeoutError: locator.waitFor: Timeout 5000ms exceeded.
Call log:
  - waiting for getByText('Generating your response').first() to be hidden
    14 × locator resolved to visible Generating your response…


Still thinking. Deep Think extended reasoning can take 3-5 minutes on complex architectural questions. Let me wait properly.


### Result
Waited for 120
### Ran Playwright code
```js
await new Promise(f => setTimeout(f, 120 * 1000));
```
### Open tabs
- 0: (current) [Google Gemini](https://gemini.google.com/app/960b95c7445299a9)
- 1: [Settings](chrome://settings/)
### Page
- Page URL: https://gemini.google.com/app/960b95c7445299a9
- Page Title: Google Gemini
- Console: 7 errors, 6 warnings
### Snapshot
```yaml
- <changed> generic [ref=e233]:
  - generic [ref=e238]:
    - ref=e240 [unchanged]
    - ref=e244 [unchanged]
    - generic [ref=e246]:
      - ref=e247 [unchanged]
      - button "Collapse" [ref=e295] [cursor=pointer]:
        - img [ref=e296]: expand_more
  - generic [ref=e263]:
    - generic:
      - generic:
        - generic:
          - generic:
            - generic:
              - button "Listen":
                - img: volume_up
          - menu
    - generic [ref=e264]:
      - ref=e269 [unchanged]
      - generic [ref=e271]:
        - button [ref=e305]:
          - generic [ref=e307]:
            - status:
              - generic: Show thinking
            - img [ref=e309]: expand_more
        - ref=e272 [unchanged]
        - paragraph [ref=e312]:
          - text: I'm on it. Responses with Deep Think can take some time, so check back in a bit.
          - generic [ref=e315]:
            - generic [ref=e316]:
              - img [ref=e317]: mindfulness
              - img [ref=e320]
            - generic [ref=e325]:
              - generic [ref=e326]: Generating your response…
              - generic [ref=e327]: Check back later
```


Still in Deep Think reasoning. This is a very complex question — it's still working. Let me wait another 2 minutes.