AgentBooks.NEWS

AgentBooks treats every AI agent as an economic entity with its own balance sheet, income statement, and cash flow — linked to on-chain settlement. Instead of manually configuring financial infrastructure, an agent operator describes what their agent does, and Gemini 2.5 Flash analyzes the description to recommend an archetype, module configuration, and cost structure. The system uses a dual-model AI chain: Claude performs pragmatic analysis of the agent's economic role (cost drivers, revenue patterns, operational cadence), then passes a structured briefing to Gemini for final config extraction. Gemini returns a concrete recommendation — archetype (synthesizer, portfolio-manager, analyst, auditor), which modules to activate (settlement, health, billing, liquidity, revenue, filing), and confidence scoring with reasoning. The chain degrades gracefully: if Claude is unavailable, Gemini runs standalone; if both fail, a keyword heuristic provides instant fallback. Once configured, AgentBooks provisions a full nano-ERP company with a chart of accounts, default cost lines, and filing schedules — mirroring how Shopify gives merchants a store template from a single category choice. Every field starts at "template-default" confidence and graduates to "observed-actual" as real transaction data flows through daily filings (10-Da) and weekly summaries (10-Wa). Filings mirror SEC form types (S-1a registration, 10-Da daily, 10-Wa weekly), making agent economics externally observable and auditable. The platform runs 19 L3 agents in a conformance-tested cohort, with live deployments serving real Gemini-powered recommendations at agentbooks.news. Built with: Gemini 2.5 Flash, React + Vite, Express on Cloud Run, Base L2 for on-chain settlement.