An Interactive Agent for Requirement-Driven Candidate Sourcing

Finding people from a natural-language description (``ML engineers transitioning to research roles in biotech'') is increasingly delegated to LLM agents and framed as information retrieval. We argue that it is fundamentally a requirements engineering task: such a request is an under-determined requirement with implicit constraints, many valid answers, and no acceptance criterion, so useful answers require eliciting, validating, and verifying the requirement before search can matter. We present sys, to our knowledge the first interactive, requirements-driven candidate-sourcing agent (it elicits, validates, retrieves, and verifies a vague people-request into a justified slate through bounded elicitation, workflow templates, a two-stage commit protocol, and bidirectional termination guards) and bench, a benchmark that runs the requirements lifecycle (criteria-anchored validation, multi-model evidence-grounded oracle construction, and cost-aware verification). Across 21 systems and all 691 requirements, sys dominates breadth (100% coverage at 2.5× the yield) and is near-orthogonal to the field, with 90% of the people it returns are surfaced by none of 20 strong LLM-plus-web baselines combined. Beyond breadth, an evidence-grounded judging of every system shows sys recalls the most relevant real people: 0.241 of the union pool, 1.9× the next system, with a bootstrap 95% interval disjoint from every baseline. sys is thus the strongest sourcing engine (the deepest real, reachable candidate pool), while precision-ranking LLMs serve as~complementary verifiers.