Method I: Agent-Guided Iterative Structural Design. -- We developed an autonomous feedback loop governed by a decision-making agent to navigate the design space iteratively. Each design cycle begins with backbone generation using RFdiffusion, conditioned on specific hotspots and Complementarity-Determining Region (CDR) lengths. This is followed by sequence recovery targeting specific binder regions using both ProteinMPNN and SolubleMPNN.The generated candidates undergo rigorous validation using Ablang2 for perplexity assessment and a comprehensive multiparametric scoring function. This scoring function evaluates candidates based on iPAE, pDockQ, dSASA, DeltaG per residue, unsaturated hydrogen bonds, and shape complementarity (sc) .To optimize local substructures, the top three candidates from each batch are subjected to an inverse folding refinement step via ProteinMPNN, which modifies specific residue positions based on their individual per-residue performance in the cited metrics. At the conclusion of each generation cycle, a comprehensive report is synthesized and fed back to a Llama 3 agent (specifically optimized for bioengineering tasks). The agent reasons over these results to algorithmically adjust hyperparameters for the subsequent design iteration.