Designing a Human-in-the-Loop Approval Matrix for an AI Agent That Drafts B2B Quote Replies

Description and use

<p>If you let an LLM agent draft replies to inbound B2B quote requests (RFQs), the hard part is not the prompt. It is deciding, in code, which drafts may go out on their own, which need a human click, and which the agent must never write at all.</p> <p>This post walks through the approval matrix pattern we use with small export sales desks: a declarative policy file, a field-level classifier, and a gate function that sits between the model and the outbox. The examples are in Python and YAML, but the pattern is tool-agnostic.</p> <h2> The failure mode we are designing against </h2> <p>An RFQ re

Problem

<p>If you let an LLM agent draft replies to inbound B2B quote requests (RFQs), the hard part is not the prompt. It is deciding, in code, which drafts may go out on their own, which need a human click, and which the agent must never write at all.</p> <p>This post walks through the approval matrix pattern we use with small export sales desks: a declarative policy file, a field-level classifier, and a gate function that sits between the model and the outbox. The examples are in Python and YAML, but the pattern is tool-agnostic.</p> <h2> The failure mode we are designing against </h2> <p>An RFQ re

Outcome

<p>If you let an LLM agent draft replies to inbound B2B quote requests (RFQs), the hard part is not the prompt. It is deciding, in code, which drafts may go out on their own, which need a human click, and which the agent must never write at all.</p> <p>This post walks through the approval matrix pattern we use with small export sales desks: a declarative policy file, a field-level classifier, and a gate function that sits between the model and the outbox. The examples are in Python and YAML, but the pattern is tool-agnostic.</p> <h2> The failure mode we are designing against </h2> <p>An RFQ re

Evidence

Designing a Human-in-the-Loop Approval Matrix for an AI Agent That Drafts B2B Quote Replies

Source