Selecting an encoding configuration for Hyperdimensional Computing (HDC) determines whether a model fits the memory budget of an Internet of Things (IoT) device, yet the knowledge needed to make that choice is dispersed across thousands of papers in a form no optimizer can query. The configuration space is large and each evaluation is expensive, so exhaustive search is infeasible; sequential optimizers are the standard remedy, but they begin from an uninformed prior and spend early evaluations exploring blindly. We present an agentic Retrieval-Augmented Generation (RAG) recommendation system that converts the HDC literature into an informed starting point for this search. A corpus of 1,518 HDC papers is indexed multimodally. A vision-language model (VLM) describes 1,755 figure regions extracted from source PDFs, making plotted evidence such as accuracy against dimension curves retrievable alongside prose and tables. Given a task profile stating the dataset characteristics, target hardware, objectives, and memory budget, the agent retrieves evidence under an allocation that reserves context for figures, and returns an encoding configuration with supporting citations. That configuration seeds the first trial of a constrained multi-objective search over accuracy and inference memory, which then refines it, and feasibility is enforced by external measurement rather than by the model. On network intrusion detection (CICIDS2017) and bearing fault diagnosis (CWRU), the literature-grounded start raised the hypervolume of the recovered Pareto front by 17.4% and 19.1% over an identically budgeted search from a random draw, at no additional evaluation cost. A natural extension is to let the system perform the data preprocessing that the practitioner now supplies, so that a deployment can be described end to end from raw sensor streams.
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