Verifiability-Weighted Autonomy: A Conceptual Framework for Governing Agentic Artificial Intelligence in Business-to-Business Credit Risk Management

Firms that sell to other firms on credit now face a new design choice. Software agents built on large language models can gather evidence, draft credit memos, release held orders and chase overdue invoices. Yet there is little guidance on how much of this work an agent should do without a person. Most research on artificial intelligence in credit asks how accurate a model is, and most of it studies consumer lending. It says little about the decision rights of autonomous agents in business-to-business trade credit, where much activity sits outside consumer-protection and bank model-risk rules. This conceptual paper addresses that gap. It integrates research on trade credit, credit scoring, language-model agents, levels of automation, agency theory and model-risk governance. From this synthesis it builds the Verifiability-Weighted Autonomy framework. The central claim is simple: the autonomy granted to an agent should rise with the verifiability of the evidence behind a decision and fall with the decision's financial exposure and irreversibility. The paper defines four autonomy tiers and a transparent allocation rule. It maps twelve common trade-credit decisions onto the tiers. It proposes a Propose–Verify–Dispose architecture in which language-model agents propose, deterministic checks verify, and policy rules or people dispose. It offers seven testable propositions and four measures for future empirical work. For managers, the framework turns a vague question, "can we trust the agent?", into a concrete design rule that fits existing delegation-of-authority practice.