Payman AI

Payman AI provides agentic banking under programmable human control. Each agent receives an isolated USD, USDC, or test wallet, containing the impact of a compromised workflow. A policy engine evaluates payments and can route higher-risk requests to people for approval.

Key Features

• Isolated Wallets: Separate balances limit the blast radius of each agent.
• Policy Engine: Set transaction caps, daily limits, recipient lists, approval thresholds, and geographic rules.
• Human Review: Blocked or large payments can be escalated for approval.
• Developer Access: REST API, Python and TypeScript SDKs, and an MCP server.
• Sandbox: Test agent behavior using fake money before production.
• Compliance Foundation: Payman is SOC 2 and PCI compliant.

Use Cases

Who Should Use This Tool?

• Agent Builders enabling controlled autonomous payments.
• Banks and Enterprises deploying financial agents with oversight.
• Framework Users working with LangChain, CrewAI, or AutoGen.

Problems It Solves

1. Shared-wallet risk: Isolation prevents one agent from reaching every balance.
2. Unbounded spending: Policies inspect recipients, amounts, frequency, and geography.
3. Premature production use: The sandbox supports safe payment testing.

Pricing

Advantages & Unique Selling Points

Compared to Competitors:

1. Wallet isolation: Risk is segmented per agent rather than pooled.
2. Detailed controls: Policies cover limits, recipients, approvals, and geography.
3. Human fallback: Exceptions can pause for review instead of simply failing.

What Makes It Stand Out:

• USD, USDC, and test-wallet options.
• Fifth Third Bank custody and Stripe processing partnerships.
• SDK, API, MCP, and major agent-framework integrations.

User Reviews

"Per-agent wallets make incident containment concrete instead of theoretical." Illustrative security-engineer perspective, editorially composed

"The fake-money sandbox lets us test approval paths before an agent can move real funds." Illustrative agent-developer perspective, editorially composed

Getting Started

Quick Start Guide

1. Open the sandbox: Create test wallets with fake balances.
2. Define policies: Add caps, limits, recipients, and review thresholds.
3. Connect an agent: Use REST, an SDK, MCP, or a framework integration.
4. Validate approvals: Exercise blocked and human-reviewed transactions.

Integration

Integrates with:

• Python and TypeScript applications
• LangChain, CrewAI, and AutoGen
• MCP-compatible agents and REST clients

Frequently Asked Questions

Which balances can wallets hold?

Payman supports USD, USDC, and test wallets.

Can humans approve large payments?

Yes. Approval thresholds enable human-in-the-loop review.

Is a sandbox available?

Yes. Developers can test flows using fake money.

Alternatives

• Signets: On-demand cards and virtual accounts.
• Zoro: Governance across multiple payment rails.
• Skyfire: Combined agent identity and payments.

Tips & Best Practices

1. Use one wallet per agent: Preserve containment and attribution.
2. Test denied payments: Verify policy violations reach the correct reviewer.
3. Raise limits gradually: Base production authority on sandbox evidence.

Conclusion

Payman AI offers a controlled path from test payments to production banking. Isolated wallets, multidimensional policies, and human approvals make it well suited to organizations that need agent autonomy without surrendering financial oversight.