Many people have described the “year of AI agents” this year, as these AI systems that can carry out tasks for users are especially useful to adapt to enterprise workflows. At Servicenow’s annual knowledge 2025 conference, the company unveiled a new model in partnership with NVidia to pursue AI agents.
April Nemotron 15B
On Tuesday, Servicenow and Nvidia launched Aprel Nemotron 15B, a new, open-source Reasoning Language Model (LLM), which was designed to give low delay, low estimation costs and agent AI. According to the release, the model was trained on the domain-specific data of Nvidia Nemo, Nvidia LLAMA Nemotron Post-Training Dataset, and Servicenow.
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The biggest path to the model is that it packages advanced logic abilities in a small size. This model makes the model run cheaper and sharp to run as Nvidia Nim Microservice on Nvidia GPU infrastructure, while still distributing enterprise-grade intelligence companies.
The company shared that the APRL Nemotron shows promising results for its model category in the 15B benchmark test, confirming that the model agent may be a good fit to support AI workflows.
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Argument abilities when using agent AI are particularly important, as in these automated experiences, AI serves for the last user in various settings. Since it is functioning without human direction, it needs to make some processing or argument to determine it to determine the best.
Joint data flywheel architecture
In addition to the model, both companies also unveiled a joint data flywheel architecture – a feedback loop that collects data from interactions to further refine the AI model. According to the architecture release, Servicenow integrates workflow data fabric and selects NVidia Nemo microservices.
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This allows joint architecture companies to use enterprise workflow data to ensure that the data is processed in a safe and timely manner, with the railing required for the safety of customers to refine its argument model more, and give them control what they want. Ideally, according to the company, it will feed in the manufacture of highly individual, reference-individual AI agents.
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