Continuous Mission Intelligence: An Architectural Requirement for Coherent Command Across Heterogeneous Autonomous Systems

Abstract

Defense artificial intelligence has advanced quickly across four layers: operational data integration, command and control, fleet-scale coordination, and platform autonomy. This paper argues that the success of those layers has produced a distinct problem that none of them addresses. As reasoning distributes into sensors, platforms, software agents and command applications, a force can become locally intelligent without becoming collectively coherent. Continuous mission intelligence is defined as the ability of a distributed human-machine system to maintain a current and reconstructable mission state across time, systems and operating conditions, and to use observed consequences to improve the next operating cycle. Six leading systems and programmes are assessed against that definition: Palantir AIP and Gotham, Anduril Lattice, Shield AI Hivemind, the US Army NGC2 programme, NATO digital transformation, and DARPA DICE. Each addresses a major layer of the stack; none addresses continuity end to end. The paper then identifies seven mechanisms by which mission coherence fractures at machine speed, derives eight architectural properties a continuity layer must exhibit, argues that coherence requires federation rather than centralisation, and proposes a two-cycle evaluation protocol that tests whether a second operating cycle begins better informed than the first. This is a position and architecture paper. It presents no empirical validation, no deployment data and no performance claims. The author declares a competing interest as co-founder and Chief Executive Officer of Rebootix AI, Inc.