Your customers have better things to do than chase answers.

Think about the work involved in choosing a service business. Open a few websites. Figure out who serves the area. Find evidence of the right experience. Work out whether the service is actually what you need. Fill in a form, then wonder whether it reached anyone.

A customer might ask their AI assistant: “Find someone who can help with window treatments for my home, compare the options, and help me request a consultation.” That is a practical job with several decisions inside it. A list of names only gets the customer part of the way there.

Kodecite's view is simple: make each of those steps easier, whether the customer does it themselves or asks an AI assistant to help. The opportunity is less friction, better-informed inquiries, and a clear start to the relationship.

In its July 2025 ChatGPT agent launch, OpenAI described a system that could research across websites and use tools to take actions, with user control and permission for consequential steps. That is a documented example of the shift from answering a question to helping finish a task. Read the original OpenAI announcement.

Connections to business software are developing too. Anthropic introduced the Model Context Protocol to connect AI assistants with external data and tools. A protocol provides a way to connect; a useful business workflow still needs accurate information, permission, and a result that means something. Read the MCP introduction.

These developments support a practical direction, rather than a claim that every customer already uses an agent or every AI assistant can complete every task. We do not need a hardware launch date or a universal adoption forecast to start improving the customer journey.

Take that window-treatment inquiry. An AI assistant needs to establish that the business offers the relevant work and serves the home's location. It should find evidence the customer can inspect, then explain the consultation process and the information required.

If the customer authorizes the request and a supported capability is available, their AI assistant can submit it. The business checks the information and returns the actual result. If the request reached the team, say that. If scheduling still needs a conversation, make that clear.

The less visible details matter just as much. An accidental repeat should not become a second lead. A changed request should not reuse an old confirmation. A failed delivery should not be reported as success. When a person needs to take over, the customer should know whom to expect and what remains open.

That is the kind of convenience worth building: a customer can make progress without surrendering the decisions that matter, and your team gets a request it can use.

Your website remains important. People use it directly, and AI assistants may read it through search or a browser. Its visible content should agree with structured business information, external profiles, and any published capability. A polished page that contradicts a form or service-area rule creates work for everyone.

Search has its own requirements. Google says its AI search features do not require special AI files or special schema markup, and appearance is not guaranteed. That is a good reason to focus on useful, accessible content rather than treating a file as a shortcut to recommendations. Read Google's guidance for site owners.

Discovery files can still have a role. The llms.txt proposal describes a way to provide concise information and links for AI assistants. A business-specific agent.json can describe identity and capabilities. Neither is a universally adopted business-action standard, and neither makes a request happen by itself.

A real action needs a supported route, such as a form, API, or tool connection, with its own validation, authorization, and result. Which route is appropriate depends on the business and the AI assistant involved. On Kodecite.ai, our discovery files describe the business; this site does not currently accept autonomous agent submissions.

An established service business has valuable detail that often lives in the owner's head: the jobs you do best, the questions that reveal fit, the work you decline, and the reasons clients trust you. Bringing that knowledge onto the website gives both customers and their AI assistants a better basis for a decision.

Ownership matters because those facts change. Your service area expands. Your offer becomes more specific. A policy or contact changes. You should own the website, code, and accounts that publish your business information, and know who will keep them accurate. Active capabilities also have service costs and maintenance dependencies that need to be understood.

The useful measures are close to the customer: can they establish fit, find the evidence, make the request, and understand the reply? Discovery is worth observing too, but a screenshot of an AI answer and a successfully handled customer request establish different things.