AI agent development services: chatbots and agents inside your product

Our AI agent development services put a chat or an agent inside a product that already has data and users: a catalogue, a B2B platform or an internal system. The AI answers from your data, and the rules sit in code and on the server. This work is part of our AI software development.

A project covers:

• a chat or an agent working on your product's data
• a separate AI service that your application calls
• tools the agent can use, each with a description and a schema
• rules enforced on the server
• tests that try to push the model off its task
• support after launch

For a product platform in the renovation and interior fittings market we run an AI chat in the application and a product-search agent, and the work is ongoing. On our own CMS, AI agents create pages, briefs and SEO changes through a production MCP server that has run since 9 July 2026. The server side has its own page, MCP server development, and putting AI into an existing product is covered in AI integration services.

This work is for product owners and CTOs of companies whose product holds real data. They want an AI chat or an agent that works on that data and stays inside the rules they set.

Building an AI chatbot begins with what the chat should know. On the renovation platform the chat answers the user, and the product-search agent next to it searches the texts and metadata of many products at once, across the offers of many suppliers. When a user asks about a price, the agent fetches the price information, and the chat's answer contains the product's source URL and the price from the platform.

The message passed to the AI carries the customer's context: name and phone number. The conversation can then move on to customer service. We reviewed the chat's performance on real conversations, in successive rounds.

In the product-search agent, Python code builds the search query and the model handles the conversation. When a search finds nothing, the agent repeats it with changed parameters.

The chat and the agent run in a separate AI service. The main application calls it through its own service on the backend. The AI part can therefore be measured, changed or switched off without touching the rest of the product. A flag set at project level in the application, ai_enabled, controls whether the AI is on. We describe this architecture in AI integration services.

For an agent that creates and saves things in your system, we put the rules on the server. On our own MCP server the rules an agent must not break are response codes:

1. Creating a page without a brief returns HTTP 400. The brief is the description of a page, tied to a goal. A person approves it before an agent writes content.
2. Writing content before a person approves the brief returns HTTP 409.
3. Publishing with audit errors returns HTTP 409. The errors include facts outside the approved list, client names covered by an NDA and no verdict from an independent critic agent.

No tool on the server has a parameter that skips these gates.

Every request from an agent carries the token of one person or integration and lands in a call log in the admin panel. On 20 October 2026 the server had 164 tools. Agents reach it through the open Model Context Protocol. According to Anthropic's announcement of 9 December 2025, ChatGPT, Cursor, Gemini, Microsoft Copilot and Visual Studio Code support it. Anthropic created the standard, and its assistant Claude works with it too.

We also run a public endpoint, https://ordergroup.co/mcp. It needs no token, only reads our case studies and sends a project enquiry, and it is listed in the official MCP registry as co.ordergroup/public. How the server works in practice is in our article MCP vs API: what we learned running an MCP server in production.

Where a wrong output costs something, we keep a person in the loop. On the Centrum Komunikacji platform, speech recognition writes a draft of the text live, a transcription specialist corrects it, and only the corrected text reaches the user, as a chat message. The feature runs in production. Encrypted fields, the option to delete a user's data and an event log are among the measures that protect personal data.

Our own agents follow the same pattern: a person approves the brief before an agent writes any content.

We test a chat and an agent like the rest of the application. On the PSONI project the AI simplifies official letters. QA runs jailbreak tests that try to pull the model off its task, for example with a request for an apple pie recipe, and repeats them with every new iteration.

When you choose an AI agent development company, look at what you can check.

• The AI can be switched off. The chat and the agent run in a separate AI service, and a project-level flag decides whether the AI is on.
• The rules sit on the server. On our MCP server, HTTP 400 and 409 responses enforce them, and every request carries a token and lands in a call log.
• Jailbreak tests run with every new iteration.
• The code is yours. Copyright passes to you with each payment, within the scope of use set in the contract.
• Support continues after launch, because models, prompts and the data they work on keep changing. See support and maintenance.

A project starts with a free consultation or a free discovery workshop, where we use our own AI toolkit. We reply to a message sent through the form within 48 hours on working days.