---
name: al-617-split-freight-invoice-exceptions-across-three-ai-agents-o
description: "AWS published an Oct 30, 2026 case study on Worldwide Express (ShipStation Global), which used Amazon Bedrock multi-agent exception handling inside its SpeedShip TMS. Three agents cover invoice matching, pricing variance and accessorial validation against carrier-specific rules. Across 40+ LTL carriers the agents close about 10,000 exceptions a week (~50%) with under 1% error; active exception backlog fell from $15M to $4M, with ~$10M freed cash flow and a 4-week faster time-to-cash. Use when the user wants help with multi-agent freight invoice exceptions (finance) for a small business."
---

# Split freight invoice exceptions across three AI agents: one matches the shipment, one checks the contracted rate, one validates surprise fees, then a person approves the note before it posts

AWS published an Oct 30, 2026 case study on Worldwide Express (ShipStation Global), which used Amazon Bedrock multi-agent exception handling inside its SpeedShip TMS. Three agents cover invoice matching, pricing variance and accessorial validation against carrier-specific rules. Across 40+ LTL carriers the agents close about 10,000 exceptions a week (~50%) with under 1% error; active exception backlog fell from $15M to $4M, with ~$10M freed cash flow and a 4-week faster time-to-cash.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- The material the task is about (pasted text, notes or data)

## Instructions

Do the task below with the user's inputs in place of each [bracket].

1. Pull the last 60–90 days of freight or carrier invoice exceptions and group them into: line-item mismatch, pricing variance vs contracted rate, and unexpected accessorials (liftgate, residential, reweigh).
2. For each group, write the decision rules a reviewer already uses, plus where the proof lives (purchase order, goods receipt, delivery events, carrier tariff).
3. Set up three AI agents (or three clear roles in one agent workflow) on Amazon Bedrock, Claude or ChatGPT enterprise connected to your TMS: Matching, Pricing Variance, Accessorial Validation.
4. Give each agent only its tools and that carrier's billing rules. Require a plain-language note: what was found, what action is recommended, and the evidence.
5. Keep a human approve step in the same UI your FinOps team already uses. Start with one exception type and one carrier; raise automation only after error rate stays under your threshold.
6. Dashboard automation rate, error rate, resolution time and human overrides by carrier every week, and feed rejections back into the prompts and knowledge base.

## Safety checks (share these with the user)

- Check every figure, price and total against your own records before anything is sent, posted or booked. A person approves refunds, discounts, payments and price changes.
- Read and edit the draft before it reaches a customer or goes public. If you later automate this prompt, start with "draft and notify me", not "send automatically".
- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: Amazon Bedrock, your TMS (SpeedShip or similar) APIs, carrier tariff/knowledge docs, FinOps approval UI
- Based on this real-world example: https://aws.amazon.com/blogs/machine-learning/how-worldwide-express-automated-freight-exceptions-with-amazon-bedrock/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-617-split-freight-invoice-exceptions-across-three-ai-agents-one/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/