What happens when a customer arrives with an AI assistant and asks it to use your product?
The request might be simple: find a replacement part, check appointment availability, or prepare a service request. The customer knows the outcome they want. Your software needs to give the assistant a reliable way to help accomplish it.
That is a useful way to think about the agentic AI era. An agent can use connected tools to take actions, inspect the results, and continue toward a goal. For a business with an existing app or website, the opportunity is to make valuable workflows accessible through those tools.
I would begin with a concrete question: Can an authorized assistant complete one useful task in your product, and can you verify what actually happened?
Consider an appointment-booking website. A person can read the calendar, select a service, choose a time, and submit a form. An assistant needs to understand the same choices, supply the right information, and distinguish an available appointment from a confirmed booking. A vague success message can leave both the assistant and the customer unsure whether anything was saved.
Several technologies are opening different routes into these workflows:
- WebMCP is a proposed browser standard that lets websites expose structured actions to compatible agents. A site could describe a product-search tool, including the inputs it accepts, and run the search through its existing page logic. WebMCP remains experimental, and browser and agent support need to be checked for the intended environment. Chrome’s WebMCP overview
- Mobile MCP is a third-party automation project that exposes tools for operating mobile apps: opening an app, inspecting accessibility controls, tapping, typing, and capturing the screen. It can be useful for development, testing, and controlled automation on connected devices or simulators. Mobile MCP project
- Native app action frameworks let developers describe specific capabilities to the operating system. Apple’s App Intents supports integrations with Siri and other system experiences. Android’s AppFunctions provides an experimental mechanism for authorized assistants and apps to discover and execute functions. Apple’s App Intents overview, Android AppFunctions
These approaches have different deployment and discovery models. That distinction matters when someone says their app is “AI ready.”
A Mobile MCP development setup can run on a computer and operate an installed app through a simulator. It does not automatically become part of the app-store download. A website’s WebMCP actions belong to the browser page. A service integration can expose business capabilities through an MCP server connected to an assistant.
The Model Context Protocol provides a way for a connected client to discover available tools and their descriptions, then invoke them with structured inputs. The connection has to exist before the assistant can use those capabilities. MCP tool discovery and invocation
ChatGPT and Codex plugins provide one packaging route. A plugin can contain skills, an MCP server, or both, with optional interface components. The plugin is the installable package; its MCP server can supply the actual tools. Supported capabilities still depend on where the plugin runs. OpenAI’s plugin architecture
For an existing business application, I would assess readiness in the following areas:
- Choose a useful workflow. Start with a task customers or staff already perform, with a clear beginning and observable result.
- Describe the available actions clearly. Inputs, required fields, and outcomes should have explicit meanings. Accessible labels also help when automation operates the existing interface.
- Preserve the user’s permissions. Every operation should enforce the same account and business rules as the ordinary product workflow.
- Make success observable. Confirm the resulting application state, such as the saved request or selected record. A tool returning “success” is only one piece of evidence.
- Handle interruptions deliberately. Expired sessions, unavailable data, failed requests, and repeated attempts need predictable behavior. Retrying a request should not accidentally create duplicate work.
- Give people appropriate control. Users should be able to review consequential actions, understand what changed, and recover when a task fails.
- Test the intended environment. Record which app, browser, operating system, and assistant combination was exercised. Recheck important workflows as those components change.
Much of this work strengthens the product for human users too. Clear forms, understandable failures, reliable navigation, and consistent business rules make an application easier to use and maintain.
The first investment can be small: review the existing system, choose one workflow, establish its current behavior, and test the most appropriate integration. Measure task completion, mistakes, interruptions, and the amount of human intervention required. Expand when the evidence supports it.
At Bill Vivino Technology, I approach this as an application-engineering problem. The useful starting point is your product’s architecture and the work people need to accomplish. From there, we can evaluate a browser tool, a mobile automation connection, a native app action, or a service integration against a specific outcome.
If you want to explore whether your app or website is ready for agentic AI, get in touch. Bring the existing product and one workflow you would like an assistant to help complete. That gives us something concrete to investigate, implement, and verify.
Technology status checked November 1, 2026. WebMCP and Android AppFunctions are evolving; availability should be verified for each intended deployment.