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Description

Google has introduced the Agent Payments Protocol (AP2), a collaborative open protocol developed with over 60 diverse companies in payments, fintech, and technology, including Mastercard, PayPal, Adyen, Coinbase, and Etsy, aimed at facilitating secure transactions led by agents across various platforms. This new protocol builds upon previous open standards such as Agent2Agent (A2A) and the Model Context Protocol (MCP) to ensure that when an AI agent processes a payment on behalf of a user, it adheres to three essential criteria: authorization, to confirm that the user has explicitly consented to the specific transaction; authenticity, to verify that the purchase intended by the agent aligns with the user's actual intent; and accountability, to maintain transparent audit trails and assign responsibility in the event of any errors or fraudulent activities. In order to uphold these standards, the protocol incorporates mandates, which are cryptographically signed digital contracts that are supported by verifiable credentials, ensuring a high level of security and trust in agent-led transactions. The implementation of AP2 represents a significant advancement in the realm of digital payments, aiming to enhance user confidence in automated financial interactions.

Description

Achieve enhanced precision and control through LangGraph, enabling the creation of agents capable of efficiently managing intricate tasks. The LangGraph Platform facilitates the development and scaling of agent-driven applications. With its adaptable framework, LangGraph accommodates various control mechanisms, including single-agent, multi-agent, hierarchical, and sequential flows, effectively addressing intricate real-world challenges. Reliability is guaranteed by the straightforward integration of moderation and quality loops, which ensure agents remain focused on their objectives. Additionally, LangGraph Platform allows you to create templates for your cognitive architecture, making it simple to configure tools, prompts, and models using LangGraph Platform Assistants. Featuring inherent statefulness, LangGraph agents work in tandem with humans by drafting work for review and awaiting approval prior to executing actions. Users can easily monitor the agent’s decisions, and the "time-travel" feature enables rolling back to revisit and amend previous actions for a more accurate outcome. This flexibility ensures that the agents not only perform tasks effectively but also adapt to changing requirements and feedback.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Accenture Billing & Payments Engine
Amazon Bedrock AgentCore
Amazon Web Services (AWS)
American Express Go
Astra DB
Checkout.com
Cohere
Coinbase
Coinbase Cloud
Dell Technologies Cloud
Intuit Field Service Management
LangChain
LangMem
Mem0
Mesh
MetaMask
Python
QUICPay
Salesforce
Worldpay

Integrations

Accenture Billing & Payments Engine
Amazon Bedrock AgentCore
Amazon Web Services (AWS)
American Express Go
Astra DB
Checkout.com
Cohere
Coinbase
Coinbase Cloud
Dell Technologies Cloud
Intuit Field Service Management
LangChain
LangMem
Mem0
Mesh
MetaMask
Python
QUICPay
Salesforce
Worldpay

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol

Vendor Details

Company Name

LangChain

Founded

2022

Country

United States

Website

www.langchain.com/langgraph

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