ROME, N.Y. - The U.S. Air Force Research Laboratory (AFRL) is expanding its artificial intelligence (AI) research priorities to include the orchestration of kinetic and non-kinetic effects.
The newest addition to the AFRL's Broad Agency Announcement (BAA FA8750-23-S-7006) for command-and-control AI research calls for prototype technologies capable of planning and synchronizing electronic warfare, cyber warfare (CW), information warfare (IW), and traditional kinetic operations within a unified framework.
Rather than treating each mission area independently, the laboratory is seeking systems that optimize effects across multiple domains to improve lethality and survivability in contested environments.
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"The modern battlefield is increasingly complex," the solicitation states, adding that future planning systems must transition from "domain-specific operations to a truly integrated planning and orchestration capability for both kinetic and non-kinetic effects." AFRL said the objective is to provide commanders with tools capable of coordinating multi-domain operations "with unprecedented speed and precision."
Command and control
The research area represents the latest evolution of AFRL's broader effort to develop AI-enabled command-and-control technologies capable of operating across distributed, resilient architectures. While the announcement is primarily focused on software development, the technical requirements have direct implications for military electronics suppliers developing embedded computing, AI acceleration, secure networking, and mission processing hardware.
The solicitation outlines a vision in which AI systems operate across geographically dispersed command-and-control nodes, managing large volumes of data while assisting operators in making time-critical decisions under degraded communications conditions. Multiple technical areas focus on federated AI deployment, software-defined command and control, collaborative autonomy, and AI-assisted battle management.
Interopterability
One technical area seeks development of federated AI toolsets capable of sharing machine learning models, data, and workflows across multiple organizations while maintaining interoperability among diverse AI components. AFRL envisions common standards that enable third-party developers to contribute AI capabilities without requiring a single underlying software platform.
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Another area addresses software-defined distributed command and control, where AI would dynamically orchestrate missions across dispersed command nodes operating under contested communications. AFRL notes that future Agile Combat Employment concepts will require automated coordination of planning and operational processes while balancing resiliency, resource utilization, and mission timelines.
The BAA also expands research into autonomous collaborative aircraft through an "Autonomous Aerial Mission Manager" (A2M2), a decentralized software capability intended to reassign missions among teams of autonomous aircraft when communications with higher headquarters are interrupted or aircraft are lost during combat operations. The software would use peer-to-peer messaging and onboard decision-making to maintain mission effectiveness in denied, degraded, intermittent, and limited communications environments.
AI assistance
Separately, AFRL continues development of "Air RAID," an AI-enabled battle management assistant designed to help commanders manage long-range kill chains involving thousands of friendly assets, threats, and targets simultaneously. The laboratory envisions AI assisting operators by continuously assessing battlespace conditions, prioritizing threats, recommending weapon-target pairings, and evaluating mission constraints that would otherwise overwhelm human battle managers.
The newly added Technical Area 11 extends those concepts beyond decision support by seeking integrated planning across electronic attack, cyber operations, information operations, and kinetic strike options. AFRL said proposed systems will be evaluated on their ability to improve planning speed, optimize allocation of multi-domain resources, and generate courses of action that outperform traditional planning approaches conducted separately within each warfare discipline.
Collectively, the research areas reflect an architectural shift toward distributed AI operating at multiple echelons rather than centralized command systems. Success will depend not only on advances in artificial intelligence software but also on the underlying computing infrastructure needed to execute AI workloads across resilient tactical networks. That includes high-performance embedded processors, AI accelerators, secure communications, mission computers, and edge computing platforms capable of supporting machine learning applications in contested operational environments.
The AFRL's updated BAA is available at https://sam.gov/workspace/contract/opp/5cd1dec3f39b44eb87cae4bd15984120/view.
