Building Reliable AI Agents in R: From Prototype to Production
AI agents that answer questions about data need to do more than impress in a controlled demo. Their responses can vary between runs, and language models can make arithmetic errors even when the answer sounds confident. A webinar from Jumping Rivers will show how to build agents in R that hand calculations to existing functions, then examine the operational details that matter when other people start using the tool.
Principal Data Scientist Pedro Silva will lead the free online session on Thursday, 19 November 2026, at 1:15pm UK time. The focus is practical: how to connect a conversational interface to calculations in R, and how to think about testing, oversight and user separation as an application grows beyond a personal experiment. Register for the webinar.
Keep calculations in R
The design puts the language model in charge of interpreting a question and choosing which function to call, while R performs the calculation. That division matters because language models generate text from patterns; they aren't dependable arithmetic engines. An agent can still misunderstand what a user is asking, but routing the computation to an established R function gives the system a defined way to produce the result.
Here's the thing: separating interpretation from computation doesn't make an agent automatically reliable. The selected function must fit the question, the data must be handled correctly, and the result still needs to be presented in a way the user can understand. But it does constrain one common failure mode: asking a model to invent or calculate a figure in natural language and trusting the fluent answer.
The webinar will cover how to build this kind of agent and what changes when a prototype becomes a tool for multiple users. That transition is often where an attractive demo meets less glamorous questions about repeatability, state and accountability. This is more significant than it looks: a system that can call a trusted function still needs a way to show what it did and to catch cases where it chose poorly.
Plan for real users
Production brings questions that a single-user demo can avoid. Separate conversations need to remain separate, so one user's context doesn't shape another user's answers. Responses may also change between runs, which makes testing less straightforward than checking whether a fixed input always produces one fixed sentence. Teams need to consider what counts as a correct answer, how much variation is acceptable, and how failures will be spotted.
Silva will also cover recording an agent's actions and applying safeguards. A record of which functions the agent called can help a team understand how it reached an answer, especially when the final response is incomplete or wrong. Safeguards can limit what the agent is allowed to do, though their value depends on how well they match the tasks and data involved. And this is the part most people overlook: a conversational interface can make a system feel simple while leaving its underlying workflow difficult to inspect.
If you're working in this space, the useful question isn't only whether an agent can answer a sample prompt. It's whether the people responsible for the application can test its behavior, trace its actions and manage user conversations after deployment. A working prototype is a start. A tool others can depend on requires operational care.
Not magic. Engineering.
Who should attend
The session is aimed at data scientists, R developers and others building or deploying AI agents. It should suit people exploring agents for the first time, as well as teams preparing an existing application for a wider audience. The subject also has a broader appeal for anyone weighing where a language model should make decisions and where conventional software should do the work.
That said, the description points to a practical session rather than a promise that every deployment problem has a standard fix. Teams will still need to make choices based on their own data, users and tolerance for inconsistent responses. The value is in understanding the design questions before a demo becomes a service people rely on.
Implications for agent development
Putting R functions behind an agent offers a clear division of responsibility: the model interprets and routes, while code handles calculations. Similar systems typically work best when that boundary is explicit and when teams can inspect the steps between a user question and the result. This approach won't remove ambiguity from natural-language questions, but it gives developers a more concrete place to validate the computation.
For organizations considering AI agents, the production details are part of the product. Conversation handling, testing, action records and safeguards affect whether users can trust an answer and whether a team can diagnose a problem. The numbers here are underwhelming if someone expects a single technique to solve reliability; the more useful lesson is that dependable behavior comes from combining model judgment with established code and careful operations.
Webinar details
- Topic: Agents in Production: From a Chatbot to a Tool You Can Rely On
- Speaker: Pedro Silva, Principal Data Scientist at Jumping Rivers
- Date: Thursday, 19 November 2026
- Time: 1:15pm UK time
- Location: Online
- Cost: Free
Further learning
Jumping Rivers also offers discounts tied to webinar attendance. Attend two webinars to receive 20% off tickets to the AI in Production 2027 conference; attend more than two to receive 20% off a Jumping Rivers public training course. These offers give attendees a route to continue learning, though the webinar's central subject remains the practical work of making agents usable beyond a demo.
