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Enterprise

Enterprises are significantly increasing their investments in AI agents, integrating these solutions into their core business and organizational processes. However, despite the enthusiasm, enterprises face major hurdles and have a strong need for robust solutions to address identity, permission and delegation, data privacy, and auditability challenges, particularly for high-stakes and inter-enterprise applications.

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B2B Procurement

B2B procurement today is burdened by fragmented systems, manual handoffs, and high trust and compliance overhead across enterprises. While core processes such as supplier selection, purchase order creation, fulfillment confirmation, invoicing, and payment are well defined, they remain slow, error-prone, and difficult to automate safely due to data-sharing risks, approval constraints, and audit requirements. An AI agent–driven procurement flow on T3N addresses these challenges by enabling enterprise AI agents to transact on behalf of their organizations under explicit, policy-bound delegation, with secure data custody, verifiable inter-agent communication, and auditable execution across buyer and supplier systems.
High level AI agent–driven B2B procurement architecture (Light mode)
A typical AI agent–driven B2B procurement transaction flow between two enterprises AI agents on T3N can look like this:
  1. Enterprise buyer (i.e., data owner) stores its payment info (e.g., enterprise account, stablecoin wallet, or payment gateway key) once in T3N.
  2. The Enterprise buyer authorizes a Buyer AI Agent to procure goods or services within predefined constraints (e.g., approved suppliers, SKU list, pricing bands, delivery terms, and spending limits).
  3. The Buyer AI Agent prepares a purchase order (PO) via the enterprise ERP system using pre-agreed commercial terms.
  4. The Buyer AI Agent sends the PO to the approved Supplier AI Agent via T3N.
  5. The Supplier AI Agent receives the PO, validates it, and confirms acceptance, delivery schedule, and fulfillment details back to the Buyer AI Agent via T3N.
  6. Upon delivery, the Buyer AI Agent confirms receipt, and the Supplier AI Agent issues an invoice through its ERP system.
  7. Before payment execution, the Buyer AI Agent again verifies that the supplier remains approved.
  8. For the final payment step, instead of accessing payment keys directly, the Buyer AI Agent submits a payment instruction to T3N, which securely delivers the pre-configured payment info to the payment platform or enterprise system.
  9. The payment platform executes the transaction, and T3N processes and sanitizes the response before returning a confirmation to the Buyer AI Agent and Supplier AI Agent, ensuring sensitive financial data is never exposed to either agent.

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Payroll

Payroll today is constrained by sensitive employee data, fragmented HRIS and payroll systems, approval workflows, banking integrations, tax reporting requirements, and strict audit obligations. While recurring payroll processes such as timesheet validation, compensation calculation, deduction handling, benefits reconciliation, tax withholding, and payment execution are well defined, they remain difficult to automate safely because AI agents must not be given direct access to employee PII, bank account details, payroll provider credentials, or treasury payment keys. An AI agent–driven payroll flow on T3N addresses these challenges by enabling enterprise AI agents to execute payroll tasks under explicit, policy-bound delegation, with secure custody of sensitive payroll data, verifiable approvals, and auditable execution across HR, payroll, banking, benefits, and tax systems. A typical AI agent–driven payroll transaction flow within an enterprise on T3N can look like this:
  1. The enterprise payroll team (i.e., data owner) stores sensitive payroll execution data once in T3N, such as payroll provider credentials, treasury payment authorization keys, employer tax account information, and approved payroll policy constraints.
  2. Employees or HR systems store required employee payroll data, such as legal identity details, tax forms, benefits elections, direct deposit information, and compensation records, with access governed by explicit policies.
  3. The enterprise authorizes a Payroll AI Agent to prepare and run payroll within predefined constraints, such as eligible employee groups, payroll calendar, approval thresholds, compensation rules, benefit deductions, tax jurisdictions, and funding limits.
  4. The Payroll AI Agent retrieves non-sensitive payroll inputs from the HRIS, time tracking system, benefits platform, and finance system, then calculates gross pay, deductions, reimbursements, taxes, and net pay.
  5. The Payroll AI Agent submits the prepared payroll run for required human or system approvals through the enterprise workflow system.
  6. After approval, the Payroll AI Agent validates that the payroll run still satisfies the delegated policy constraints, including employee eligibility, payment totals, approval status, and funding limits.
  7. For the final payroll execution step, instead of accessing employee bank details, payroll provider credentials, or treasury payment keys directly, the Payroll AI Agent submits a payroll execution instruction to T3N.
  8. T3N securely delivers the required sensitive payroll data directly to the payroll provider, banking platform, benefits administrator, or tax system needed to complete the transaction.
  9. The payroll provider and connected systems execute salary payments, benefit contributions, tax withholdings, and required filings.
  10. T3N processes and sanitizes the execution responses before returning payroll status, exception details, and audit records to the Payroll AI Agent, ensuring sensitive employee and financial data is never exposed to the agent.

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Individual

AI agents are rapidly becoming essential tools for individuals automating complex, multi-step tasks that traditionally required human intervention, such as booking travel or managing financial portfolios. However, for these agents to complete “last-mile” transactions (e.g., confirming a flight booking with a payment), they often require access to highly sensitive user data, like payment details, passport numbers, credential, or API keys. Current systems often require users to grant agents full access to this data, creating significant privacy and security risks. If the agent or the platform it resides on is compromised, the sensitive data is exposed, as seen in the UnitedHealth Group and Middle East call center breaches. A typical AI agent-driven transaction flow for individuals on T3N can look like this:
  1. You (i.e., data owner) store your private user data once in T3N.
  2. You authorize an AI agent (e.g., a travel booking agent) to perform a specific action (e.g., “Book me a flight to Dubai using my preferred credit card”).
  3. The AI agent processes the request (e.g., finding the cheapest direct flight).
  4. When the AI agent is about to initiate the last-mile transaction (e.g., actual booking), instead of exposing the private user data to the agent, the AI agent interacts with T3N, which then securely delivers the required private user data directly to the third party (e.g., the airline’s booking system).
  5. The third party (e.g., the airline’s booking system) receives the data, completes the transaction.
  6. T3N processes the third-party response (e.g., from the airline’s booking system) before forwarding it, ensuring any private user data is processed first and not returned to the AI agent.
Examples of automating transactional tasks with AI agents for individuals:
  • Online financial product applications (credit cards, personal loans).
  • Insurance claim management.
  • Customer support interactions.
  • Job searching tasks.
  • Patient registration.
  • Online shopping.