Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/agentic_systems.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 |
| Branch | codex/build-online-github-ani-irqa7y |
| Path or archive member | daemon/datasets/agentic_systems.json |
| Original SHA-256 | 46a7562ceea0eb7990201caaeb333a903045f71dc90c3f6366e9bb0acf8a8b24 |
| Git blob SHA | 164253840f9d708d814ee4dae4852c69d24cc54c |
| Reading-copy SHA-256 | 46a7562ceea0eb7990201caaeb333a903045f71dc90c3f6366e9bb0acf8a8b24 |
Every recorded occurrence
These 17 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-0etkns | e250b19d446bdffd542c9eea31cead12e30800d6 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/agentic_systems.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
Recovered source transcript
Download complete source text ↓331 lines · 7.6 KB
[
{
"name": "Agentic Revenue Orchestrator",
"domain": "agentic_systems",
"description": "Coordinates autonomous agents that blend RAG, CAG, and KAG flows to execute multi-channel revenue plays without manual handoffs.",
"controller": "LLM executive controller",
"trigger_keywords": [
"agent",
"autonomous",
"workflow",
"playbook",
"automation"
],
"frameworks": [
"LangChain",
"Haystack",
"LlamaIndex"
],
"toolchain": [
"Prompt Orchestration Hub",
"Experiment Grid",
"Scenario Simulator"
],
"data_interfaces": [
"Vector database (Pinecone)",
"Feature store",
"Postgres analytics"
],
"knowledge_sources": [
"Campaign briefs",
"Customer journeys",
"Experiment memory"
],
"revenue_programs": [
"New customer acquisition",
"Expansion automation",
"Recurring revenue uplift"
],
"risk_controls": [
"Runbook approvals",
"KPI guardrails",
"Budget checkpoints"
],
"maturity_signals": [
"Full agent workflow coverage",
"Prompt governance",
"Automated feedback loops"
],
"base_maturity": "advanced",
"metrics": [
"agent_cycle_time",
"automation_roi",
"playbook_success_rate"
]
},
{
"name": "Database Fusion Layer",
"domain": "data_connectivity",
"description": "Bridges transactional databases, vector stores, and feature stores so the ANI keeps decisions synchronized with live data.",
"controller": "Data mesh steward",
"trigger_keywords": [
"database",
"sql",
"postgres",
"feature store",
"vector"
],
"frameworks": [
"In-database ML",
"BigQuery ML",
"Feature store APIs"
],
"toolchain": [
"psycopg2",
"BigQuery ML",
"Feast"
],
"data_interfaces": [
"Postgres",
"BigQuery",
"Feature store",
"Vector database"
],
"knowledge_sources": [
"Revenue warehouse",
"Attribution tables",
"Feature registry"
],
"revenue_programs": [
"Predictive CLV",
"Dynamic pricing",
"Lead scoring"
],
"risk_controls": [
"Role-based access",
"Row-level lineage",
"Data drift monitors"
],
"maturity_signals": [
"SQL automation",
"On-demand feature materialization",
"Vector sync cadence"
],
"base_maturity": "foundational",
"metrics": [
"data_freshness_hours",
"feature_serving_latency",
"clv_model_accuracy"
]
},
{
"name": "Predictive Revenue Lab",
"domain": "machine_learning",
"description": "Runs ML pipelines that cover CLV, churn, segmentation, and personalization to focus spend on profitable cohorts.",
"controller": "Model operations lead",
"trigger_keywords": [
"predictive",
"ml",
"model",
"churn",
"segmentation"
],
"frameworks": [
"Vertex AI",
"SageMaker",
"Azure ML"
],
"toolchain": [
"AutoML",
"Experiment tracker",
"Feature store"
],
"data_interfaces": [
"Customer warehouse",
"Product telemetry",
"Marketing automation"
],
"knowledge_sources": [
"CLV priors",
"Churn curves",
"Segmentation playbooks"
],
"revenue_programs": [
"Upsell campaigns",
"Retention save flows",
"Personalized bundles"
],
"risk_controls": [
"Bias audits",
"Explainability reports",
"Model retrain calendar"
],
"maturity_signals": [
"Lift vs control",
"Model freshness",
"Closed-loop personalization"
],
"base_maturity": "advanced",
"metrics": [
"clv_uplift",
"churn_reduction",
"segmentation_precision"
]
},
{
"name": "Market Intelligence Reactor",
"domain": "market_operations",
"description": "Synthesizes stock, crypto, and commodity telemetry with marketing signals to steer capital deployment.",
"controller": "Quant revenue strategist",
"trigger_keywords": [
"trading",
"market",
"quant",
"volatility",
"portfolio"
],
"frameworks": [
"Quant research",
"High-frequency trading",
"Event-driven trading"
],
"toolchain": [
"Algorithmic trading bots",
"Risk engine",
"Market data feeds"
],
"data_interfaces": [
"CoinGecko",
"CoinCap",
"Blockstream",
"Stock market APIs"
],
"knowledge_sources": [
"Macro dashboards",
"Event calendars",
"Alternative data lake"
],
"revenue_programs": [
"Automated trading",
"Hedging overlays",
"Capital allocation"
],
"risk_controls": [
"VaR guardrails",
"Stop-loss automation",
"Compliance logging"
],
"maturity_signals": [
"Latency benchmarks",
"PnL consistency",
"Risk-adjusted returns"
],
"base_maturity": "elite",
"metrics": [
"sharpe_ratio",
"pnl_run_rate",
"drawdown_probability"
]
},
{
"name": "Agentic Customer Command Center",
"domain": "customer_experience",
"description": "Deploys agent teams that run support, sentiment analysis, and journey orchestration to grow loyalty and revenue.",
"controller": "CX automation lead",
"trigger_keywords": [
"support",
"sentiment",
"journey",
"bot",
"customer"
],
"frameworks": [
"Agent orchestration",
"Sentiment AI",
"Prompt management"
],
"toolchain": [
"Service desk automations",
"Sentiment monitors",
"Prompt hub"
],
"data_interfaces": [
"CRM",
"Helpdesk",
"Social listening",
"Knowledge base"
],
"knowledge_sources": [
"Support macros",
"Voice of customer",
"Feedback themes"
],
"revenue_programs": [
"Retention uplift",
"Cross-sell campaigns",
"Advocacy loops"
],
"risk_controls": [
"Escalation matrix",
"SLA monitoring",
"Compliance review"
],
"maturity_signals": [
"Containment rate",
"Sentiment swing",
"Journey completion"
],
"base_maturity": "foundational",
"metrics": [
"nps_delta",
"retention_uplift",
"support_automation_rate"
]
},
{
"name": "Prompt Intelligence Fabric",
"domain": "prompt_operations",
"description": "Manages prompt engineering, cache-augmented generation, and knowledge governance to keep responses precise.",
"controller": "Prompt strategist",
"trigger_keywords": [
"prompt",
"cag",
"kag",
"cache",
"knowledge"
],
"frameworks": [
"Prompt management",
"Cache-augmented generation",
"Knowledge-augmented generation"
],
"toolchain": [
"Prompt registry",
"Cache manager",
"Knowledge router"
],
"data_interfaces": [
"Prompt catalog",
"Cache layer",
"Knowledge base",
"Vector index"
],
"knowledge_sources": [
"Playbooks",
"Knowledge hubs",
"Prompt experiments"
],
"revenue_programs": [
"Faster go-to-market",
"Content monetization",
"Freelance agent services"
],
"risk_controls": [
"Prompt approval flow",
"Cache invalidation",
"Hallucination monitoring"
],
"maturity_signals": [
"Cache hit rate",
"Knowledge accuracy",
"Prompt latency"
],
"base_maturity": "advanced",
"metrics": [
"prompt_success_rate",
"cache_hit_ratio",
"knowledge_accuracy"
]
}
]