Bridging the Gap: A Human-Orchestrated Proto-AGI Workflow for Cross-Domain Structural Engineering Assessment

Proto-AGI describes the intermediate stage of artificial intelligence between narrow task-specific tools and fully autonomous general intelligence. This paper presents the first formal operationalisation of Proto-AGI traits within a cross-domain digital workflow for structural engineering and demonstrates it through a four-domain computational ecosystem applied to seismic vulnerability assessment. Viktor.ai processes cone penetration test data to stratify a three-layer soil profile, identifying a compressible intermediate stratum at 5 to 12 m depth with amplification characteristics in the 0.3 to 0.7 second period range, based on the depth and stiffness contrast of the weak layer rather than a formal site response analysis. The Fayaz RotD script computes orientation-independent RotD50 and RotD100 response spectra for two contrasting ground motion records: the near-fault Northridge record (RSN 1086, Mw 6.69) delivers RotD50 = 2.00 g and RotD100 = 2.79 g at the structural natural period of 0.41 s, a 39.6% directional uplift; the moderate-distance Kobe record (RSN 1107, Mw 6.9) delivers RotD50 = 0.591 g and RotD100 = 0.795 g at the same period. OpenSeesPy nonlinear dynamic analysis of a five-storey reinforced concrete frame produces peak inter-storey drifts of 0.45% and 0.34% under the two records respectively, both within the FEMA 356 Immediate Occupancy threshold of 1.0%. A 3.4-fold spectral demand difference produces only a 1.32-fold drift difference, reflecting the combined effects of frequency content, pulse characteristics, duration and nonlinear structural response under the two contrasting records. The Viktor.ai RC Section Analyzer yields a curvature ductility factor of 2.3 under ACI 318-25, identifying deformation capacity as the governing constraint under more severe future demands. These four findings form a causal chain connecting site conditions, spectral demand, structural response and sectional capacity that no single domain produces independently—the emergent ecosystem intelligence that defines Proto-AGI in structural engineering practice.