Causal Abelv1.1.6

A user asks to analyze the causal drivers of a specific asset (e.g., AAPL, Bitcoin) or how two nodes are connected. The agent classifies as direct_graph, follows the direct-graph workflow, and returns a structured causal read with verified edges.

A user asks to evaluate a major life decision (e.g., buying a house, choosing a career, moving to a new city) using Abel's proxy signals from financial and crypto nodes. The agent classifies as proxy_routed, generates hypotheses, screens via graph, observes, and grounds with web searches.

A user asks about a decision with a horizon >3 years (e.g., 'What will AI jobs look like in 10 years?'). The agent switches to structural mode, uses web search as primary evidence and graph only for validation, avoiding momentum-style observation.

A user's opportunity thesis depends on a recent unverified claim (e.g., a leak about a product launch). The agent performs minimal premise verification, and if unanchored, reframes as conditional analysis ('if this is true, then...') before proceeding with Abel.

Subscription tier for individuals or enterprises to get causal reads on personal, career, or investment decisions. Includes authentication, unlimited proxy-routed analyses, and periodic web grounding.

Offer the causal graph and probe endpoints as an API for robo-advisors, wealth management apps, and trading platforms. Monetized per call or via flat licensing fee.

Sell one-time deep-dive reports for long-horizon decisions (macro trends, industry shifts) combining web research and graph validation. High-ticket, delivered as PDF or interactive dashboard.