Agents and agent mode
An agent is the smartest way vragen.ai answers questions, and the direction the product is evolving in. An agent doesn't answer in one go, but takes multiple reasoning steps: it determines what it needs, searches your knowledge base in a targeted way and builds its answer from that. For harder questions this produces better and better-substantiated answers.
In the article From a search query to a reasoning agent (in Dutch) we show visually how an agent reaches an answer.
What does an agent do extra?
Where the classic system runs one search and answers based on that, an agent can take steps of its own while reasoning:
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split a broad question into subquestions and look them up separately;
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search multiple times if the first results aren't good enough;
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combine information from different sources into one answer.
An agent sticks closely to your instructions and stays close to your own sources. That makes the answers predictable and well substantiated.
An agent doesn't always have to think longer. You can make it work just as simply and quickly as a classic system, and for more complex questions it uses its extra reasoning steps.
Switching without losing your work
Already done optimization rounds on your current system? They're not lost. When moving to an agent, your instructions and tweaks come along. A technique like splitting up questions keeps working too; an agent just does it in its own way, as one of its reasoning steps.
The safest route is trying it out before you go live. In your test environment or the playground you select the agent at the top and compare its answers with those of the classic system. Once you're satisfied, we put the agent live for you.
Agents are the future of vragen.ai: by the end of 2026 all environments will use them and we'll be migrating existing systems. So switching is the route either way, and as described above your optimization work isn't lost in the process.
What an agent may use
Per agent you configure which part of your knowledge base it may consult. That's useful if you have multiple websites or languages in one environment:
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Give each agent access to only its own content, for example a Dutch and an English agent side by side.
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Or let all agents access everything, and use instructions to steer how they handle the different languages or brands.
Which approach fits depends on how your content is structured. We're happy to think along. See also Managing content in your knowledge base.
Connecting external data sources (MCP)
Besides your own knowledge base, an agent can also consult an external data source via an MCP server (Model Context Protocol). Think of your own vector database or an external API.
You add the MCP server in your environment and then switch it on or off per agent. That way you decide which agent may use the extra source.
This is the reverse direction of Knowledge base as an MCP server: there you offer your knowledge base as a source to an external AI client such as Claude. Here your agent uses an external source.
This is how we offer LexAgent (in Dutch), for example: a source with Dutch laws and regulations, local regulations (such as a municipality's general local bylaw), official publications and court rulings. Want to do something with this, or curious whether we can offer a particular source as MCP? Get in touch via service@vragen.ai.
Which language model does an agent use?
An agent works with the language model you choose in the agent settings. From the Professional plan onward you decide this yourself. More about this in Hosting and data storage.
Want to know more?
Want to switch, or first see what an agent delivers for your situation? Email service@vragen.ai and we'll set up an agent for you to test. See also Testing and evaluating to assess the answers.