An AI agent in finance is a system that can understand what a user is trying to do, apply the company’s finance rules, and complete an entire workflow from start to finish. Unlike a chatbot, which only replies with information, or an automation rule, which triggers a single predefined action, an AI agent can turn plain-language requests into real operational steps. For example, it can create a purchase request, apply policies, route approvals, issue a card, or prepare an expense report. It behaves like a trained operations partner that follows your company’s controls and completes the work directly inside the finance system.

Payhawk’s AI agents are built directly into the platform, which means they act inside your existing company policies, approval flows, travel rules, card controls, spend limits, and accounting setup. Because they have structured access to your actual finance configuration, they go far beyond answering questions. They can interpret the request, select the right workflow, follow the correct approval chain, complete each step, and log the entire process. A generic chatbot cannot do this because it has no understanding of your specific rules, user permissions, or financial context. Payhawk’s agents operate within these guardrails and perform real work, not just surface-level assistance.

Payhawk’s AI agents work by interpreting a user’s natural-language request, checking it against your company’s finance policies and approval logic, and then executing the task inside the platform. They can gather missing details, create and categorise requests, route approvals, issue or fund cards, prepare expenses, or chase outstanding items. They always act within the user’s own role and permissions, which is a key security mechanism. An agent cannot access sensitive information or perform any action that the employee using it is not allowed to perform. All actions are logged, monitored, and governed by Payhawk’s security standards, including SOC 2 and ISO 27001. This ensures the agents are safe to use because they operate strictly inside your company’s rules and the user’s permission boundaries.

Memory in Payhawk works on two levels. The first level is system memory, which includes everything defined in your platform setup, such as spend rules, card controls, travel policies, vendor guidelines, approval flows, and accounting mappings. The agent uses this to ensure every action is compliant. The second level is long-term conversational memory, which is personal to each user. Over time, the agent can learn preferences like typical travel times, favourite hotel styles, or preferred communication patterns. This allows the agent to offer suggestions that feel natural and tailored, without ever accessing anything the user is not permitted to see.

Payhawk’s AI agents already handle a wide range of everyday finance operations. They can manage intake for purchase, travel, and spend requests, interpret the user’s intent, and turn a simple sentence into a structured workflow. They follow the correct approval logic, chase the right people, move requests forward, and complete steps such as booking travel, issuing cards, funding cards, preparing expenses, extracting receipt data, reconciling transactions, and fetching recurring invoices from vendor portals. They can also answer common questions about cards, reimbursements, and how to use the platform. Every task they perform runs inside the rules and controls defined by your finance team.

Payhawk’s agents automate the execution of finance tasks, but they do not replace human decision-makers. They never override approval chains, spend limits, or policy requirements. When a user needs to make a judgment call or approve spending, the agent simply ensures the request reaches the right person quickly and with the correct context. Automation happens around the user, not instead of the user. This means the agents keep work moving without ever bypassing governance.

Yes. Payhawk’s agents operate entirely within the structure configured in your workspace. They follow your specific approval flows, card controls, custom request types, travel policies, budgets, vendor rules, and accounting logic. They interpret user intent and then select the correct workflow based on your setup. Because they also inherit each employee’s permissions, they cannot perform actions or access information outside the boundaries defined by your finance team.

No. Payhawk’s agents are designed to make finance teams more efficient, not to replace them. They take over the repetitive operational work that consumes time — chasing receipts, nudging approvers, preparing expenses, fetching invoices, or answering common questions. This allows finance professionals to shift their focus toward analysis, forecasting, planning, business partnership, and strategic work. Agents eliminate the draining “firefighting mode” many teams are pushed into, but human expertise and oversight remain essential.

Payhawk’s agents are trained on the platform’s product logic and the structure of common finance workflows. When enabled in your company’s environment, they use your policies, approval flows, historical requests, card rules, and accounting settings as their operational framework. We follow the same data security rules as anywhere in our product. Our own agents access our data within the security of our infrastructure, everything is contained. This also means that LLM providers (Google Gemini) do not train using our customers’ data. We consider their data privacy as our top priority and we won’t compromise with this.

Yes. Payhawk applies the same security model to its AI agents as to the rest of its platform. This includes SOC 2 Type II, ISO 27001, strict role-based access controls, encryption, and full audit logs. Because agents inherit the user’s permissions, they cannot access or act on anything the employee cannot. All actions are recorded and governed by your existing finance policies. This ensures each agent operates safely within your organisation’s security and compliance standards. We follow the same data security rules as anywhere in our product. Our own agents access our data within the security of our infrastructure, everything is contained. This also means that LLM providers (Google Gemini) do not train using our customers’ data. We consider their data privacy as our top priority and we won’t compromise with this.

AI agents in Payhawk work out of the box and can be enabled instantly. There is no separate implementation project because the agents operate within the rules, workflows, and approval logic already defined in your Payhawk setup. For travel, you simply confirm your travel policy; for procurement, you ensure your request types and approval flows are configured. After that, enabling an agent is as simple as flipping a toggle. You can choose whether the agent should operate inside the Payhawk platform or reach employees through Slack or Microsoft Teams, and it becomes available immediately.