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What an AI wingman has to control
A “wingman” is an uncrewed aircraft intended to operate with crewed fighters or with other uncrewed aircraft. Its autonomy is usually divided into three interacting layers rather than treated as one all-powerful artificial intelligence.
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Vehicle-level flight safety
At the aircraft level, sensors and flight-control software estimate the vehicle’s position, velocity and surroundings. They look for nearby aircraft, terrain and other mission constraints, then replan a trajectory when the current path becomes unsafe. This layer handles immediate separation and flight stability: keeping a drone from colliding with a teammate, the ground or an unexpected object while still respecting the route and maneuver limits in its mission plan.
Military formations add a difficult requirement. A wingman must hold tactical spacing and geometry relative to a lead aircraft while also avoiding terrain, other aircraft and threats. A safe maneuver therefore cannot simply be “turn away”; it must preserve formation intent where possible, obey aircraft performance limits and avoid creating a new hazard for another member of the team.
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Team-level coordination
Collaborative autonomy lets aircraft share observations and status, divide mission tasks and coordinate routes or tactics. A vehicle that detects a hazard can contribute that information to the group, while a planning system reallocates objectives if one aircraft loses a sensor, communications path or route. Task allocation may be centralized through a lead node, distributed among the aircraft, or hybrid, with different decisions made at different levels.
This sharing is what allows several drones to behave as a team rather than as independent autopilots. It also creates a dependency: inconsistent sensor data, delayed messages or a damaged vehicle can force the group to fall back to preplanned behavior and conservative separation rules.
Airspace-level deconfliction
Civilian beyond-visual-line-of-sight (BVLOS) operations add a strategic airspace layer. The Federal Aviation Administration’s UAS Traffic Management (UTM) guidance describes a collaborative ecosystem for flight planning, authorization, surveillance and conflict management. UTM services can manage drone-to-drone collision risk across operations, but they are not a military wingman controller. They help organize airspace before and during flights; they do not replace the aircraft’s onboard emergency avoidance.
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How the layers work together when a route changes
- Sense: Onboard systems detect aircraft, terrain and other constraints. Networked teammates contribute their own tracks and status where a communications link is available.
- Assess: The flight-control and mission software evaluates whether the planned path still satisfies separation, formation, airspace and mission requirements.
- Replan: The aircraft or team generates an alternative trajectory, spacing maneuver or task assignment. Immediate flight safety takes precedence over an obsolete route.
- Coordinate: The revised plan is shared with teammates or the supervising human when communications permit, reducing the chance that two aircraft choose conflicting evasive actions.
- Execute and monitor: Flight controls fly the maneuver while sensors check whether the hazard is actually clearing. If the situation changes again, the loop repeats.
These layers reduce risk; they do not guarantee safety in every environment. A public source has not established a single collision-success rate that applies to all wingman designs, weather conditions or threat scenarios.
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What happens when communications are degraded or denied?
A resilient wingman cannot assume a continuous data link. In the Defense Advanced Research Projects Agency’s (DARPA) Collaborative Operations in Denied Environment (CODE) program, vehicles retained the intent of their mission plan when communications were degraded or denied, rather than requiring a human to issue every low-level command. The design goal was graceful degradation: aircraft could continue bounded navigation and coordination using the information already available, while accepting that they would have less current information about teammates and the environment.
During a 2018 test series, CODE used up to six live and 24 virtual unmanned aircraft. DARPA reported that the vehicles shared information, cooperatively planned and allocated mission objectives, made coordinated tactical decisions and reacted to a dynamic, high-threat environment with minimal communication. The program description said:
“Multiple CODE-equipped unmanned aircraft would navigate to their destinations and find, track, identify, and engage targets under established rules of engagement—all under the supervision of a single human mission commander.”
That result demonstrates a test capability, not a promise that every future wingman can operate indefinitely without a link. The evidence specifically concerns degraded or denied communications. It does not establish that all systems can navigate accurately in every GPS-denied condition, nor that they can safely perform every mission without external updates. GPS resilience, sensor quality, map accuracy and the aircraft’s contingency policy remain design-specific questions.
When a link returns, a team must also reconcile state: which tasks were completed, which tracks are stale and whether a teammate’s independent maneuver changed the safe route. Robust architectures use explicit mission intent and status, rather than assuming that every aircraft shares an identical real-time picture.
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How wingmen avoid one another during formation and combat maneuver
- Relative separation: Each aircraft monitors the position and motion of nearby teammates and maintains a protected volume around them.
- Formation constraints: A planned station, route or tactical geometry limits which evasive maneuvers are acceptable.
- Priority rules: Immediate collision avoidance can override a lower-priority task or route segment; the team then replans around the disruption.
- Shared tracks: Cooperative observations extend awareness beyond one aircraft’s line of sight and help prevent two vehicles from maneuvering into the same gap.
- Fallback behavior: If communications or a sensor fails, the aircraft follows its retained mission intent and predefined safety behavior instead of waiting indefinitely for a command.
Military systems must combine these controls with threat response. A maneuver that is safe geometrically may expose an aircraft to a threat or break a formation’s tactical purpose, so the planner weighs flight safety against mission constraints. Public descriptions do not provide a universal algorithm or a fleet-wide collision probability; implementations and test conditions differ.
Who decides whether a drone may fire?
The public record distinguishes mission-level use-of-force decisions from lower-level aircraft control. DARPA’s ACE program describes a division in which a human can retain engagement strategy, target selection and prioritization, and the choice of weapon or other effect. The autonomous system can then execute bounded aircraft maneuvers and engagement tactics within those human-set constraints. ACE’s stated path is to establish trust in limited within-visual-range behaviors before attempting more complex engagements.
This is human-machine task allocation, not a claim that a pilot manually flies every turn. A human commander may approve the objective and boundaries while the aircraft handles timing, positioning and other rapid calculations. The exact approval workflow, sensor thresholds and abort conditions are architecture- and mission-specific.
The Department of Defense’s 2023 policy update requires autonomous and semi-autonomous weapon systems to be designed so commanders and operators can exercise appropriate levels of human judgment over the use of force. It also requires use with appropriate care and in accordance with the law of war, treaties, safety rules and applicable rules of engagement. “Autonomous” therefore describes how the aircraft performs assigned functions; it does not mean unconstrained authority to select and attack people or objects.
What rules of engagement look like in the software
Rules of engagement (ROE) are operational instructions that define when and how force may be used. In an autonomous system, they can be represented as constraints on the mission plan and engagement logic: approved target categories, geographic or temporal boundaries, identification requirements, weapon restrictions, and conditions that require human confirmation. The aircraft’s autonomy can optimize movement and timing only inside those limits.
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A typical control chain separates:
| Decision or function | Typical allocation described publicly | Why the distinction matters |
|---|---|---|
| Engagement strategy | Human commander or operator | Sets the intended military effect and acceptable risk. |
| Target selection and prioritization | Human remains responsible in the ACE description | Prevents the aircraft from independently redefining the mission. |
| Weapon or effect choice | Human decision in the ACE allocation | Connects the action to ROE, law-of-war obligations and collateral-risk judgments. |
| Aircraft maneuver and engagement tactics | Delegated to the autonomous system within bounds | Allows rapid positioning and coordinated flight without requiring stick-by-stick control. |
These are design principles, not proof that every aircraft exposes the same interface or requires the same confirmation at the final step. Commanders remain accountable for setting lawful constraints and supervising the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Military wingmen and civilian BVLOS drones are not the same system
| Aspect | Collaborative-combat wingman | Civilian BVLOS operation |
|---|---|---|
| Primary safety problem | Maintain formation and tactical separation while avoiding terrain, aircraft and threats. | Avoid crewed and uncrewed aircraft across shared airspace. |
| Coordination layer | Vehicle and team autonomy share information, allocate objectives and coordinate routes or tactics. | FAA UTM provides collaborative planning, authorization, surveillance and conflict management. |
| Link-loss behavior | CODE demonstrated retention of mission-plan intent under degraded or denied communications. | Operators need an approved detect-and-avoid means for crewed aircraft; the specific fallback depends on the operation. |
| Use of force | ACE and DoD policy place appropriate human judgment over weapon use. | UTM is an airspace service and does not authorize combat decisions. |
| Evidence cited publicly | DARPA tests, Air Force training and ongoing autonomy-architecture flight testing. | FAA guidance for BVLOS detect-and-avoid and UTM concepts. |
UTM concepts are useful for explaining strategic deconfliction, but presenting UTM as the control system for a military formation would be misleading.
Are AI wingman drones operational yet?
The answer depends on what “operational” means. Public milestones show live testing and architecture integration, not universal field deployment or identical autonomy across manufacturers.
Live control of Valkyrie aircraft
In July 2025, the U.S. Air Force reported that pilots in F-16C and F-15E fighters each controlled two XQ-58A Valkyrie aircraft during an air-combat training event. The demonstration showed real-time integration of a crewed fighter with semi-autonomous aircraft in a training environment. It did not establish that every squadron has the same capability or that the drones can conduct unsupervised combat.
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Government reference architecture and flight testing
In February 2026, the Air Force reported integrating a government-owned Autonomy Government Reference Architecture across its Collaborative Combat Aircraft ecosystem. The report associated autonomy work by RTX/Collins and Shield AI with YFQ-42 and YFQ-44 flight testing. An open, government-owned architecture is intended to make autonomy components easier to integrate and evolve, but architecture integration and flight tests are milestones on the path to a fielded capability, not evidence of fleet-wide readiness.
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Royal Air Force strategy
The Royal Air Force’s March 2024 Autonomous Collaborative Platforms strategy presents autonomous collaborative platforms as a force multiplier that augments existing capability and accelerates learning. A strategy document signals intended direction and experimentation; it does not by itself certify a specific aircraft for operational deployment.
How to evaluate a wingman autonomy claim
When a manufacturer or service announces a demonstration, ask seven questions before treating it as proof of a mature system:
- Where is sensing performed? Determine which collision and target-detection functions run onboard and which require a network or external sensor.
- Who allocates tasks? Identify whether a central controller, a distributed team or a hybrid architecture assigns routes and objectives.
- What happens without GPS or communications? Separate a demonstrated communications-denied behavior from an unproven claim of full GPS-denied navigation.
- What can the human approve or veto? Look for explicit controls over target selection, prioritization, weapons or other effects, rather than a vague “human in the loop” label.
- What detect-and-avoid evidence exists? Check whether the test involved formation mates, cooperative traffic, uncooperative crewed aircraft, terrain or all of these.
- Is the architecture interoperable? A government reference architecture or open interface can ease integration, but it does not prove that every autonomy module works on every aircraft.
- What was the maturity level? Distinguish simulation and virtual vehicles from surrogate aircraft, live test aircraft, training events and certified operational deployment.
These questions prevent a successful demonstration from being mistaken for a guaranteed collision-avoidance rate, a complete kill chain or a ready-to-deploy fleet.
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AI wingmen avoid collisions through layered sensing, trajectory replanning, team coordination and, in civilian airspace, strategic deconfliction services. They can continue with retained mission intent when communications are degraded, as CODE demonstrated, but public evidence does not justify a blanket claim of GPS-independent operation. For weapons, autonomy is bounded by human decisions, rules of engagement and law-of-war requirements; the aircraft may execute approved maneuvers and tactics without independently deciding whom to attack.
As of the July 2025 and February 2026 Air Force reports, the technology is in active test, training and architecture-integration phases. Those milestones are significant demonstrations of progress, not proof that all AI wingman drones are operationally deployed or behave identically.
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