A team is adapting an NVIDIA AI Blueprint that uses an agent workflow to automate multi-step research tasks. They want the agent to call an internal knowledge-base tool rather than a public search API. Which customisation approach is most consistent with how agent blueprints are designed?
- AReplace the underlying large language model NIM with a smaller model that has been fine-tuned on internal documents so tool calls are not needed.
- BEnable automatic speech recognition on the agent input layer so that voice queries bypass the public API and route directly to internal systems.
- CRegister the internal knowledge-base as a new tool definition within the agent configuration, pointing the tool executor at the internal endpoint instead of the public API. Correct
- DWrap the entire blueprint in a diffusion-based summarisation layer that condenses public API results to match the internal knowledge format.
Why A is wrong: Fine-tuning the base model encodes static knowledge but does not replace the benefit of dynamic tool retrieval; it also discards the agent's ability to call multiple tools and is disproportionate to the goal of swapping one tool endpoint.
Why B is wrong: ASR adds a multimodal input modality and is unrelated to which retrieval tool the agent calls; it does not change how the agent resolves tool endpoints.
Why C is correct: Agent blueprints expose a tool registry where each callable tool is described by a schema and an endpoint. Adding or replacing a tool definition is the intended customisation path: the agent LLM selects tools by name and description, so updating the registry entry redirects calls to the internal service without changing the rest of the workflow.
Why D is wrong: Diffusion models generate images or audio and are not applicable to text summarisation; this option conflates multimodal generation techniques with agent tool customisation.