What is an AI agent?
“Which subscriptions am I paying for?” sounds simple. Answering it requires transaction history, recurring-charge detection and verified amounts. A language model does not inherently know a current account balance or history. It needs data and functions available for a defined purpose.
An AI agent is an application that uses a model and available tools to carry out a task. Depending on its design, it may retrieve data, run calculations, compare results and prepare an answer. The service designer determines its autonomy. An agent need not be allowed to execute every operation a user requests.
Chat is an interface for communication; an agent describes how work gets done. A conversational interface may host an agent, but a chat window alone does not demonstrate those capabilities.
MCP: a shared interface for tools
The Model Context Protocol standardises how AI applications communicate with exposed tools and context sources. An MCP server can expose a transaction retrieval function, document search or case-status lookup. The AI application accesses these through an MCP client. [1]
In finance, MCP can sit between an agent application and existing APIs. It replaces neither a bank’s API nor its business logic, and is not itself an AI model. It makes system capabilities accessible through a common protocol interface. [2]
Agents can also use tools without MCP. Choosing the protocol makes sense when a shared interface improves interoperability and future development.
Example: a question about subscriptions
In a proposed service, a user asks about recurring charges. The application determines the scope, while the access layer checks permission to read the relevant transactions. A tool returns the records; rules or an algorithm identify possible recurring charges; the model explains the result with supporting evidence.
Amounts should come from calculations over records, rather than a model’s prediction of words. Posted transactions must be distinguished from estimated future charges. A similar merchant name alone does not prove an active subscription.
A useful agent shows where an answer comes from and where its certainty ends.
Our personal finance demonstrator illustrates that interaction. It is a local simulation using synthetic data, with no AI model, live MCP server or account connection. It lets visitors explore the concept before an integration is built.
Systems must enforce permissions
Our approach begins with access boundaries: who uses the service, for what purpose, with which data and for how long. The backend should enforce permissions. An instruction in a prompt is not an access-control mechanism.
A document, transaction description or search result may contain instructions that must not be treated as user commands. We therefore design restricted toolsets, input validation, event logging and error handling. Operations that change data need their own controls; permission to read transactions does not authorise a payment.
MCP includes authorisation mechanisms, but a deployment must still enforce access correctly. Choosing the protocol is not a security certification. [3]
Where can financial institutions find value?
Potential starting points include an API documentation assistant, support for integration incidents and explanations of data made available to users. Each has different sources, permissions and quality criteria. We propose selecting one process and testing its outcome before exposing broad system capabilities.
At QRQS.eu PSA, the team’s experience in co-developing PolishAPI informs this work. Consistent interfaces, clear roles and predictable system behaviour still matter. A new way of asking questions does not remove the integration effort.
How do you start a concrete project?
First define the task and success criteria: correct amounts, traceable sources, handling time or cases requiring human intervention. Next build a prototype with test data, evaluate errors and denied access, and assess operating costs.
Before introducing real data, establish participant roles and the applicable payment-service, data-protection and AI requirements. Those depend on functionality and the operating model, rather than the MCP label. This article presents a business and architectural perspective.
Let’s discuss AI agents in your processes ↗
Sources and further reading
[1] Model Context Protocol — Architecture overview ↗[2] Model Context Protocol — Understanding MCP servers ↗[3] Model Context Protocol — Authorization ↗Sources consulted: 30 October 2026. The examples and proposed implementation approach are the author’s perspective.