AI Agent Payment OS

CashCat is an AI Agent Payment OS that lets agents automate payments for APIs, data, software, compute, and services with budgets, approvals, receipts, proofs, and audit trails.

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Inspiration

AI agents are moving from chat to action. They can research, plan, call tools, and coordinate workflows, but they still lack a safe way to spend money. Giving an autonomous agent a raw card, wallet key, or payment credential is risky

CashCat was inspired by one question:

If agents are going to work for us, how should they pay for things safely?

What It Does

CashCat is an AI Agent Payment OS.

A user gives an AI agent a business task. The agent decides whether paid resources are needed, such as APIs, data, software, compute, or services. CashCat then automates and governs the payment workflow with:

  • Budgets
  • Approval rules
  • Payment intent generation
  • Receipts
  • Spend proofs
  • Audit trails
  • Single-agent and multi-agent workflows

The core idea is:

LLMs decide. CashCat governs and executes.

How We Built It

We built a public product demo, architecture, workflow diagrams, schemas, and example agent payment flows. The demo shows a natural-language task flowing through:

  1. AI task understanding
  2. Structured spend proposal
  3. Budget and approval checks
  4. Governed payment intent
  5. Receipt and spend proof
  6. Workflow artifact generation

For the AI layer, we designed the planner around Qwen2.5-14B-Instruct running through an AMD vLLM-style inference endpoint. The model’s role is to translate a natural-language task into a structured payment proposal.

CashCat acts as the payment automation and control layer around that proposal.

What We Learned

We learned that agent payments are not just a payment problem. They are a workflow, security, trust, and automation problem.

The hard part is not simply moving money. The hard part is answering:

  • What is the agent trying to do?
  • Is this spend within scope?
  • Who authorized it?
  • What budget applies?
  • Does it require human approval?
  • How do we prove what happened afterward?

This helped us frame CashCat as more than an API. It is a payment operating layer for autonomous agents.

Challenges

The biggest challenge was making the product understandable.

At first, the project looked too much like a technical protocol demo. We had to reshape it into a product experience where users can immediately understand:

  • Give an AI agent a task
  • Let it propose paid actions
  • Let CashCat automate and govern the payment
  • Show receipts, proofs, and workflow results

Another challenge was scope. Agent payments can cover APIs, SaaS, cloud, crypto wallets, enterprise cards, and agent-to-agent transactions. We focused the hackathon demo on digital spend, while designing the architecture to support broader payment rails later.

## What Is Next

Next, we want to connect CashCat to real payment rails such as Stripe, PayPal, cards, wallets, and crypto infrastructure. We also want to expand the platform into:

  • Developer APIs and SDKs
  • Enterprise control consoles
  • Real approval workflows
  • Agent budget allocation
  • Payment reconciliation
  • Stablecoin and wallet-based settlement
  • Agent-to-agent commerce

Our long-term vision is for CashCat to become the financial operating layer for autonomous AI agents

Built With

  • amd
  • codex
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