The Evidence Closure Loop: Engineering a Bounded-Autonomous, Multi-Agent Security Staff for Evidence-Grounded Assessments and Bug Bounty

Security teams are increasingly experimenting with large-language-model agents, yet most implementations optimize for generation speed rather than decision integrity. A system that can produce a polished report faster can also produce a polished mistake faster. This practitioner whitepaper presents an architecture for a scalable security staff built around role separation, explicit evidence models, mandatory adversarial quality assurance, and bounded autonomy. The implementation uses a central chief-of-staff, specialized security SMEs, a shared evidence workspace, and an independent Security QA gate. It applies Knowledge Augmented Generation (KAG) in two distinct planes: a retrieval plane that produces provenance-preserving evidence packs, and a decision plane that links scope, vulnerability semantics, evidence strength, trust boundaries, and qualification criteria. The paper contributes a transferable operating model, a dual-plane KAG pattern, an evidence-closure discipline, a triage-first bug bounty gate, and an evaluation framework that measures decision quality in addition to cost. The MiniCISO implementation is used as a reproducible case study, not as a product pitch.