Multi-agentFinance & Investment AnalysisAdvancedFree

Accounts Receivable & Collections System.

3 agents, 3 deliverables.

Run a multi-agent workflow to produce an execution-ready accounts receivable & collections system deliverable.

WORKFLOW META
Agents3
Total tokens (avg)~500
Run time9 min
AI toolChatGPT Β· Claude Β· Gemini
Variables4
DifficultyAdvanced
CategoryFinance & Investment Analysis
SEQUENCE MAP Β· CLICK TO JUMP
Β· 01 Β·
Receivables Health Analyzer
Β· 02 Β·
Collections Process Designer
Β· 03 Β·
Invoice & Payment System Optimizer
USE CASE INPUTS

Set the workflow's inputs once.

These variables feed into every agent prompt below. Fill them once, then copy each agent in order.

{{business_type}}
Business type
Business type
Accounts Receivable & Collections System example context
{{average_invoice_value}}
Average invoice value
Average invoice value
Accounts Receivable & Collections System example context
{{current_average_days_to_collect}}
Current average days to collect
Current average days to collect
Accounts Receivable & Collections System example context
{{current_overdue_receivables}}
Current overdue receivables
Current overdue receivables
Accounts Receivable & Collections System example context
THE AGENTS

The 3-step sequence.

01
AGENT Β· RECEIVABLES HEALTH

Receivables Health Analyzer

GOAL OF THIS STEP

Assess the receivables health. Calculate: DSO (Days Sales Outstanding) vs. industry benchmark, the true cost of slow collection (cash tied up Γ— opportunity cost), the aging analysis categories (current / 0–30 days / 31–60 / 61–90 / 90+ days), the bad debt probability by aging bucket, and the total working capital impact. Identify: which customer segment is slowest to pay and why.

EXPECTED OUTPUT

DSO vs. benchmark + cost of slow collection + aging analysis + bad debt probability + slowest-paying segment

agent-01-receivables-health-analyzer.md
### Input
Business type, invoice value, DSO, overdue amount

### Task
Assess the receivables health. Calculate: DSO (Days Sales Outstanding) vs. industry benchmark, the true cost of slow collection (cash tied up Γ— opportunity cost), the aging analysis categories (current / 0–30 days / 31–60 / 61–90 / 90+ days), the bad debt probability by aging bucket, and the total working capital impact. Identify: which customer segment is slowest to pay and why.

### Output
DSO vs. benchmark + cost of slow collection + aging analysis + bad debt probability + slowest-paying segment
02
AGENT Β· COLLECTIONS PROCESS

Collections Process Designer

03
AGENT Β· INVOICE &

Invoice & Payment System Optimizer

HOW TO RUN

Three steps. 9 min.

STEP 01

Fill in the variables at the top. Copy them into a note or your tool's context window β€” every agent below uses them.

STEP 02

In your AI tool, paste Agent 1 and run it. Copy the output. Paste Agent 2 with the output appended. Repeat in order for all 3 agents.

STEP 03

At the final agent, review and refine. It outputs your finished deliverable, ready to publish or hand off.

WHAT YOU GET

The final output, end-to-end.

Receivables Health Analyzer

DSO vs. benchmark + cost of slow collection + aging analysis + bad debt probability + slowest-paying segment

Collections Process Designer

7-step escalation ladder with full communication template + sender + payment link for each step

Invoice & Payment System Optimizer

Invoice format requirements + payment terms strategy + payment method setup guide + 10-item invoice quality checklist

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The Multi-Agent Operator Pack.

100 production-ready workflows like this one. Agent prompts, variable cheat-sheets, and the operator's guide.

Free