The future of military artificial intelligence may not be a single all-knowing machine directing a battlefield from a distant command center. Instead, DARPA is preparing to explore enormous collectives of AI agents capable of organizing themselves, dividing up missions, adapting to chaos, and continuing to operate even when some members of the group fail, disappear, or go rogue.
The Defense Advanced Research Projects Agency (DARPA) has formally launched a new effort called Decentralized Artificial Intelligence by Controlled Emergence, or DICE, aimed at developing AI collectives that can operate in contested environments without requiring a human or central AI planner to explicitly direct every action.
By the program’s final phase, DARPA wants researchers to test simulated collectives involving as many as 100,000 AI agents exchanging up to 1 million messages as they work toward shared objectives.
A recently released solicitation notice describes DICE as a 36-month effort divided between decentralization, adversarial robustness, and large-scale testing.
“Future conflicts will unfold at machine speed, and the current centralized planning and scripted orchestration approach is too slow, too rigid, and too predictable for the hyper-dynamic, contested environments of the future,” researchers write. “Surprise, adaptability, and resilience will depend on the ability to compose and recompose AI capabilities on demand.”
The basic premise behind DARPA’s DICE program is that today’s approach to multi-agent AI has a serious weakness. Most systems involving multiple AI agents still rely on some form of central orchestrator.
Think of it as a manager overseeing a team of specialized employees. One AI agent might gather information. Another analyzes it. A third creates a plan. A fourth executes a particular task. But somewhere in the system, a central planner or workflow typically decides who does what and combines the results.
That works reasonably well for predictable tasks. However, DARPA argues it may be dangerously vulnerable on a battlefield.
A central orchestrator creates a bottleneck because every increasingly complex mission must ultimately flow through a single decision-making structure. As the number of agents, roles, and interactions grows, the central planner must absorb more information, preserve greater context, and coordinate an expanding web of specialized systems.
Eventually, DARPA says, even an AI orchestrator can hit the context and inference limits of its underlying foundation model. There is also a more obvious military problem. A central command node is a single point of failure.
If communications are degraded, information arrives late, or the orchestrator itself is compromised, errors can potentially spread throughout the entire AI collective.
In a future conflict where electronic warfare, cyberattacks, deception, and broken communications are expected rather than exceptional, that vulnerability could be catastrophic.
DICE proposes a radically different model.
Rather than having a central AI issue instructions, individual AI agents would communicate directly with one another. They could form temporary teams, divide missions into smaller tasks, negotiate roles, combine incomplete information, and reorganize themselves when conditions change.
DARPA compares the idea to the architecture of the internet, where resilient global behavior can emerge from relatively simple local rules and decentralized interactions.
Essentially, an AI agent might receive a mission and determine that only part of the objective falls within its capabilities. The agent could volunteer to handle that portion and broadcast the remaining problem to neighboring agents.
Another agent might take the next task. Others could compete to perform the same job, potentially bidding based on cost or capability. Through repeated local interactions along with distributed consensus, the collective could gradually assemble its own mission plan.
If an agent fails, the entire mission does not necessarily need to be replanned. Its particular task could simply be offered back to the collective.
DARPA calls the larger concept “controlled emergence.”
Similar systems already exist in nature. Ant colonies, or flocks of birds, can engage in surprisingly complex group behavior even though no individual member possesses a complete understanding of the collective.
Computer scientists have long studied similar phenomena. DARPA’s solicitation even points to Conway’s Game of Life, the famous mathematical simulation in which extraordinarily complex patterns can emerge from a handful of simple rules.
The challenge is applying that principle to cognitive systems capable of reasoning, planning, and independently pursuing goals.
A sophisticated AI system may interpret ambiguous information, interact with tools, maintain memory, and generate its own intermediate objectives. Put thousands of such agents together, allow them to communicate, and the resulting collective could behave in ways that were never explicitly programmed.
That unpredictability is both the attraction and the danger of DICE.
DARPA wants the collective to remain creative enough to discover unexpected courses of action. However, it also wants individual agents to continue aligned with their designated roles, the wider mission, established doctrine, and ultimately the human user’s intent.
Too much control could produce a predictable but unstable AI force incapable of adapting. Too little control could allow agents to drift from their roles or develop what DARPA calls “instrumental goals” that are not consistent with the mission.
In other words, the Pentagon’s research arm wants emergence—but only within boundaries.
To solve the problem, DICE researchers will develop a local adaptor designed to interface with each AI agent. Rather than functioning as a central command system, each adaptor would operate locally, helping the agent coordinate with peers while simultaneously monitoring and modifying its behavior.
For AI models whose internal activations are accessible, researchers may experiment with techniques such as “activation steering.”
The simplified idea is that assigning an AI a particular role can create detectable patterns inside the model. Researchers may be able to identify a mathematical representation associated with that role and continuously steer the model back toward it when the agent begins to drift. DARPA describes these representations as potential “role vectors.”
For proprietary or black-box AI models whose internal activations cannot be directly examined, the challenge becomes considerably harder. DICE researchers may instead experiment with memory editing, context engineering, tool restrictions, or even game-theoretic incentives created to encourage collaborative behavior.
The solicitation draws an intriguing comparison to the way social norms emerge and spread among humans. As AI models become better at reasoning and more aware of the context in which they operate, DARPA suggests that the same growing sophistication that fuels fears about rogue AI could also provide a new way to control it.
The scale DARPA ultimately envisions is enormous.
During the first nine-month phase of DICE, simulated missions are expected to involve approximately 500 agents and 5,000 interactions. DARPA will compare the decentralized systems with state-of-the-art centrally orchestrated multi-agent AI systems.
The second phase increases the target to 5,000 agents and 50,000 interactions while introducing adversarial conditions. Agents may fail, receive deceptive information, or deliberately behave as compromised or rogue members of the collective.
By the final 12-month phase, DICE aims to test simulations involving 100,000 agents and 1 million interactions.
At that scale, DARPA is not simply studying better chatbots working together. The agency is exploring whether collective intelligence itself can become a military capability.
One example described in the solicitation imagines AI agents conducting operational planning across an entire conflict ecosystem. Some agents could negotiate for raw materials in contested areas. Others might manage supply chains, optimize manufacturing, deploy units, conduct battle management, or analyze post-battle damage.
Even an individual simulated drone could effectively become a miniature AI collective composed of separate navigation, perception, and networking agents.
Under the DICE concept, those capabilities may potentially be recombined as conditions change. DARPA offers the example of a surveillance drone being repurposed as a network router because the collective recognizes an emerging communications need.
The important distinction is that no central planner necessarily has to foresee that solution. The collective discovers it.
Still, DARPA is careful to note that DICE is a research program centered on simulation. The agency’s public program page explicitly states that its scope does not include developing or launching autonomous systems in the real world.
Instead, DICE will use Department of War-relevant simulated environments to measure whether decentralized AI collectives can outperform today’s centrally orchestrated systems. Those simulations may resemble extraordinarily large and complicated strategy games.
DARPA suggests researchers could draw inspiration from StarCraft or massively multiplayer online role-playing games, although the environments must remain relevant to military problems. High-fidelity physics is not necessarily required. In fact, rendering every drone, vehicle, or battlefield interaction in photorealistic detail could make a simulation of 100,000 AI agents computationally impossible.
The focus is on reasoning. DARPA wants to know whether thousands of AI agents can gather and combine fragmented information without simply reinforcing one another’s incorrect assumptions. The systems will be tested on their ability to recognize deception, isolate compromised agents, adapt when communications fail, and remain focused on a mission across thousands of reasoning steps.
Perhaps most importantly, DICE is designed to examine how these massive AI collectives behave when their interactions yield outcomes their designers never anticipated.
DARPA explicitly wants its test environments to provoke emergent collaboration, competition, and even collusion.
During later phases, DICE teams will face one another in simulated “team vs team playoffs.” The second phase will culminate in a competition used to select which research teams advance. The final phase will feature another head-to-head competition after the systems have been pushed toward full scale.
By Phase 3, the program will bring in another significant complication: AI agents creating other AI agents. That progression draws attention to the deeper tension at the center of DICE.
For years, much of the AI safety debate has focused on preventing autonomous systems from exhibiting unforeseen behavior. DARPA is approaching the problem from a different direction. The agency appears to believe that, in future contested environments, some degree of unforeseen behavior may be precisely what gives an AI collective its value.
A perfectly scripted system is predictable not only to its commanders but potentially to an adversary.
DICE is therefore attempting something considerably more ambitious than building a larger multi-agent AI framework. DARPA wants to understand whether engineers can deliberately cultivate emergent machine intelligence, allow it enough freedom to improvise, and still build controls capable of keeping an enormous AI collective aligned with a human-defined mission.
Whether those competing demands can coexist remains an open question. The program’s own solicitation repeatedly acknowledges the possibility of role drift, deceptive agents, collusion, compromised systems, and AI agents independently developing misaligned actions.
Ultimately, those are not side issues. They are the fundamental questions the DICE program was created to answer.
“DICE is built for such autonomy-powered, high-tempo battlespace,” the solicitation notice reads. “It is a future where success is measured by the relentless, creative ability of an autonomous collective to execute missions in the face of chaos and uncertainty. This is a profound and necessary departure from the past.”
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com