The Secure Frontend — Building the “Face” and “Front Door” of Your AI Agent

1.1 Introduction: The Anatomy of an AI-Native Application

The term “AI agent” has rapidly entered the developer lexicon, often used interchangeably with “chatbot.” However, this comparison is insufficient. A chatbot typically follows a predefined script, answering questions or performing simple tasks. An AI agent, in contrast, is a more sophisticated system. It is designed to perceive its environment, plan a multi-step course of action, reason about its choices, and act autonomously by using a set of available tools. The ultimate goal of an agent is not just to provide information, but to automate complex, end-to-end tasks with minimal human intervention.

This series will deconstruct a complete, end-to-end example of such a system: the ladam2000/szakdolgozat repository. We will analyze this project not as a single, monolithic “AI app,” but as a modern, distributed system composed of three distinct, interacting layers:

The Experience Layer: The frontend where the user interacts. (Part 1) 1.

The Logic Layer: The agent’s “brain,” which defines its capabilities and reasoning. (Part 2) 1.

The Runtime Layer: The secure, scalable platform where the agent “lives” and executes. (Part 3)

This architecture represents a fundamental paradigm shift toward “Agent-Native Applications.” In this model, the AI agent is not a feature bolted onto an existing application; the agent is the application’s core value. The “business logic” is no longer defined by rigid if/then statements, but by a set of goals and tools provided to an autonomous reasoning engine. The frontend, as we will see, becomes a “cockpit” for directing this new, intelligent backend.

1.2 Deconstructing the Frontend: The User’s Cockpit

The frontend’s primary role is to provide this “experience layer.” In a modern stack like that seen in the szakdolgozat repository, this is almost certainly a React application.

The user interface (UI) itself often resembles a familiar chat window. The core logic for this involves two key React elements:

State Management: Using React hooks like useState to manage the user’s current query and the agent’s answer or streaming response.

Asynchronous Function: An async function, such as askAgent or handleSend, that is triggered when the user clicks a “Send” button.

The most critical function of the frontend, however, is securely connecting to the agent’s “brain.” This must be done in a specific, secure manner. A common mistake for beginners is attempting to call AI services (like OpenAI or Amazon Bedrock) directly from the React code. This approach is critically flawed as it exposes secret API keys in the client-side code, visible to anyone.

The correct and secure architecture, as employed in this type of application, is as follows:

The React app makes a standard fetch or axios POST request to a single, secure backend API endpoint (e.g., /api/agent). 1.

The body of this request is a simple JSON object containing the user’s message, for example: {“message”: “query”}.

This frontend architecture may appear overly simple, but this simplicity is a deliberate and powerful design choice. The frontend acts as a “dumb terminal,” a concept that mirrors the “Backend for Frontend” (BFF) pattern, but “supercharged” for AI. The frontend is intentionally decoupled from the agent’s operational complexity. It does not know—and does not need to know—if the backend is a simple Python script, a complex multi-agent “swarm” , or a stateful, long-running microVM. This abstraction is the key to a scalable and maintainable system, as it allows the backend agent logic to be re-architected, scaled, or even completely swapped (e.g., from LangGraph to Strands) with zero changes to the frontend client.

1.3 The “Auth” in Authentication: Why Your Agent Needs an Identity

Before a user can interact with the agent, the system must establish trust. This involves two distinct concepts:

Authentication (AuthN): Verifying “Who are you?” This is the process of proving an identity.

Authorization (AuthZ): Determining “What are you allowed to do?” This is the process of verifying permissions.

In a traditional web application, the system typically only needs to authenticate the user. Agentic systems, however, introduce a more complex security challenge: the management of two distinct identities and the secure link between them.

The User Identity: This is the human logging into the React application. The system must verify who they are (AuthN) and what they are allowed to ask the agent to do (AuthZ).

The Agent Identity: The agent itself is a non-human, machine entity. When it needs to perform a real-world action (e.g., “generate a stock report and save it to S3,” as seen in a similar stock-analyzer agent ), it must authenticate itself to the S3 service (AuthN) and prove it has permission to write to that specific S3 bucket (AuthZ). This is a “machine-to-machine” (M2M) flow.

The application’s primary security challenge, therefore, is managing “Delegated Access”. The user (a human) is delegating their authority to the agent (a machine). A compromised or misbehaving agent can “actively harm your systems”. The entire architecture of the szakdolgozat project is built around securely binding the User Identity to the Agent Identity for a limited, auditable, and secure session.

1.4 Practical Implementation: Securing the Application with AWS Cognito

To manage these identities, the szakdolgozat architecture leverages dedicated AWS services.

The “Front Door” (User Identity) The User Identity is managed by Amazon Cognito. This service provides a complete solution for user sign-up, sign-in, and access control. The flow works as follows:

The ****, likely using the AWS Amplify library , displays a sign-up or sign-in form.

The user enters their credentials, which are sent directly to [Amazon Cognito].

Cognito authenticates the user and returns a set of JSON Web Tokens (JWTs)—specifically, an ID Token (proving identity) and an Access Token (proving permissions).

The React app securely stores these tokens to manage the user’s session.

The “Bouncer” (User Authorization) Now, this user identity must be linked to the API request. This is where Amazon API Gateway comes in, acting as the secure “bouncer” for the agent backend.

When the user sends a prompt, the React app retrieves the JWT Access Token and attaches it to the API request in the Authorization header (e.g., headers: { ‘Authorization’: \Bearer ${accessToken}}).

This POST request is sent not to the agent’s server directly, but to the [Amazon API Gateway] endpoint.

This API Gateway is configured with a “Cognito User Pool Authorizer”.

This authorizer automatically intercepts the incoming request. Before the agent’s code is ever executed, API Gateway inspects the Authorization header, validates the JWT with Cognito, and confirms that the user is valid and has the correct permissions (scopes) for this action.

If the token is invalid, expired, or missing, API Gateway blocks the request with a 401 Unauthorized error, and the agent logic never even runs.

This architecture masterfully decouples user authentication from agent logic. A junior developer might be tempted to validate the JWT inside the agent’s Python code. This is an insecure and inefficient practice. By using an API Gateway Authorizer, the szakdolgozat architecture makes the agent logic simpler and far more secure. The agent code, when it finally runs, operates with a guarantee that the request has already been authenticated and authorized. It can focus on its core task—agentic reasoning—instead of boilerplate security checks.

We have now successfully built the “face” (React) and the secure “front door” (Cognito + API Gateway). A validated user can now send a secure prompt. In Part 2, we will explore what lies behind that door: the agent’s “brain.”