Interoperable data and intelligence flows among allied partners and operational end-users remain essential to NATO's collective defense across both conventional and hybrid threat environments. Foreign Information Manipulation and Interference (FIMI) increasingly spans multiple societal domains and information ecosystems, complicating threat characterization, persistent situational awareness, and coordinated response. Concurrent advances in AI have further lowered the barrier to conducting large-scale, AI-augmented FIMI activities -- including automated generation, personalization, and amplification of manipulative content. While frameworks such as DISARM offer a standardized analytical and metadata schema for characterizing FIMI incidents, their practical application for automating large-scale detection remains challenging. We present a framework-agnostic, agent-based operationalization of DISARM piloted to support FIMI investigation on social platforms. Our agent coordination pipeline integrates general agentic AI components that (1) identify candidate manipulative behaviors in social-media data and (2) map these behaviors to DISARM taxonomies through transparent, auditable reasoning steps. Evaluation on two practitioner-annotated, real-world datasets demonstrates that our approach can effectively scale analytic workflows that are currently manual, time-intensive, and interpretation-heavy. Notably, the experiment surfaced more than 30 previously undetected Russian bot accounts -- deployed for the 2025 election in Moldova -- during the prior non-agentic investigation. By enhancing analytic throughput, interoperability, and explainability, the proposed approach provides a direct contribution to defense policy and planning needs for improved situational awareness, cross-partner data integration, and rapid assessment of information-environment threats.