Agentic Testing

Overview

This Solution Blueprint is an AI-powered UI testing framework that uses an LLM agent with Model Context Protocol (MCP) for automated web testing. The agent interprets Gherkin-style Given-When-Then test specifications and executes them via MCP tool calls, providing real-time feedback during test execution. After completing the test run, the application generates a downloadable Pytest module that can independently re-run the same browser tests.

AMD Solution Blueprints are packaged as helm charts for deployment on a Kubernetes cluster. For development or further exploration, the source code is public and available in the Solution Blueprints GitHub repository.

Architecture

The user enters test specifications in Gherkin format (Given-When-Then syntax) through the Streamlit web UI. The UI provides real-time feedback during test execution, displaying live logs and results as the agent interacts with the browser via Playwright MCP.

Component	Role
LLM Service	OpenAI-compatible endpoint (AIM vLLM or external), used for test reasoning and interpretation
Playwright MCP Server	Exposes browser automation tools via Model Context Protocol
Streamlit UI	Web interface
Testing Agent	Python-based orchestrator that connects LLM reasoning with MCP browser automation

Key Features

• Web-based UI for entering Gherkin (Given-When-Then) test specifications

• Real-time test execution logs and progress tracking

• Browser automation via Playwright MCP server

• LLM service health monitoring in the UI sidebar

• Connects to MCP server via SSE transport with automatic tool discovery

• Pytest module generation from successful test scenarios

Getting Started

This is a quick start guide on how to deploy the blueprint. For advanced options, such as reusing an existing AIM, providing a Hugging Face token, or overriding storage classes, see Deploying Solution Blueprints with Helm or explore the advanced deployment guide.

Prerequisites

System Requirements

This blueprint can be deployed on AMD Instinct (default) and AMD Radeon. The blueprint requires the following cluster resources by default, depending on the hardware being used:

Resource	Instinct	Radeon
GPUs	1	1
CPUs	8 CPU cores	8 CPU cores
RAM	72 Gi	40 Gi

To deploy to the Kubernetes cluster, ensure the following prerequisites are met:

• kubectl: Installed and configured to communicate with the cluster

• Helm 3.17 or higher installed on your local machine

Deployment

For advanced deployment options, explore the advanced deployment guide. Solution Blueprints are packaged as OCI-compliant Helm charts in the Docker Hub registry and can be deployed to a Kubernetes cluster with a single command. Define the name (deployment name) and the namespace (Kubernetes namespace), then pipe the output of helm template to kubectl apply -f -.

Find the deployment command below. Note: You can create a namespace using kubectl create namespace <my-namespace>.

Instinct

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-agentic-testing \
  | kubectl apply -f - -n $namespace

Radeon

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-agentic-testing \
  --set global.platform=radeon \
  | kubectl apply -f - -n $namespace

Verify Deployment

To check the status of the deployment, run:

kubectl get pods -n $namespace

Wait until all pods report Running and Ready.

Connect to UI

To connect to the UI, port-forward to any chosen port, e.g., 8501. The UI will then be available at http://localhost:8501 in your browser.

kubectl port-forward services/aimsb-agentic-testing-${name}-ui 8501:8501 -n $namespace

Clean Up

When you are finished, remove the deployed resources using the same deployment command, with kubectl delete instead of kubectl apply. For example, for Instinct use the following command:

helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-agentic-testing \
  | kubectl delete -f - -n $namespace

Third-party Components

This Solution Blueprint utilizes multiple components. For third-party license information, refer to each component’s documentation. Key third-party components are listed below.

Component	License
Playwright MCP	Apache-2.0
OpenAI Python SDK	Apache-2.0
Streamlit	Apache-2.0
MCP (Model Context Protocol)	MIT

Terms of Use

AMD Solution Blueprints are released under the MIT License, which governs the parts of the software and materials created by AMD. Third-party software and materials used within the Solution Blueprint are governed by their respective licenses.