Preparing a brigade-level staff wargaming exercise the conventional way takes weeks. Exercise designers produce the scenario, write the OPORD, build the ORBAT, brief the control cell, and pre-author inject sequences for dozens of anticipated decision branches. On exercise day, experienced staff officers often reach the limits of the pre-authored scenario within the first session – they have run similar exercises before, recognise the inject pattern, and route around the intended decision pressure. The scenario that cost weeks to build is effectively exhausted before the training objective is achieved.
WARG (AI-Powered Wargaming Training) addresses this structural problem at its source. Rather than pre-authoring scenarios from a finite library of designed situations, WARG uses AI to generate multi-domain wargaming scenarios continuously during the exercise, adapting to every command decision a participant group makes. No two WARG exercises follow the same sequence. The adversary does not execute a pre-scripted plan – it responds to what the Blue side actually does, within doctrine-consistent decision logic, across all five operational domains simultaneously.
This article examines the problems WARG is built to solve, the multi-domain battlefield it simulates, the mechanics of its adaptive AI adversaries, and the practical experience of running a NATO brigade staff exercise through the platform.
What traditional wargaming gets wrong
The failure modes of traditional wargaming exercises are well understood by training staffs, even when they lack a systematic alternative. Scenario planning consumes the majority of total exercise resource – designer time, subject matter expert availability, control cell preparation – leaving relatively little resource for the training delivery itself. This ratio is inverted from what training effectiveness requires: the scenario is the vehicle, not the product, and resource invested in vehicle production is resource not invested in learning.
Pattern memorisation is the second structural failure. Military organisations are not large, and experienced officers rotate through the same training organisations repeatedly. When a brigade staff officer has run four iterations of a Baltic defence scenario in the past three years, the fifth iteration teaches them the scenario, not the underlying operational problem. They know which axis the OPFOR will use for the main effort, approximately when the electronic warfare degradation inject will arrive, and how aggressively the control cell will apply the rules of engagement. The cognitive challenge – the actual training stimulus – is severely diminished by familiarity.
Post-exercise feedback timing is the third problem. The value of feedback in skill development is time-sensitive: feedback delivered immediately after a decision is far more effective at shaping behaviour than feedback delivered three days later in a written AAR. Traditional exercises produce feedback in batch, after the full exercise is complete, after the control cell has reviewed hours of event logs, and after the instructor has written the analysis. By that point the specific decision context is no longer vivid in the participant's memory and the learning connection is weaker.
Key insight: The bottleneck in traditional military wargaming is not the quality of the scenario content – it is the pre-authoring dependency. Every decision branch the exercise must handle requires a human designer to anticipate it and write a response. AI scenario generation removes this bottleneck entirely, replacing the finite authored decision tree with an infinite generative process that produces responses to decisions the designer never anticipated.
The fourth problem is domain coverage. A skilled exercise control cell can adjudicate a complex land manoeuvre scenario. Covering the same scenario simultaneously across land, maritime, air, space, and cyberspace domains – with all the cross-domain interaction effects that multi-domain operations require – demands a level of domain expert coverage that most training organisations cannot sustain. The result is exercises that nominally cover multi-domain operations but effectively reduce to a land-centric scenario with notional OPFOR air and cyber activity injected on a schedule, rather than a genuinely interactive multi-domain competition.
The WARG multi-domain battlefield
WARG simulates five operational domains as interacting systems: land, maritime, air, space, and cyberspace. Each domain is not a separate layer added to a land-centric baseline – it is a full participant in the operational picture, with its own force disposition, decision cycles, and effects on adjacent domains. The exercise does not progress through pre-defined phases; it evolves continuously from the interaction of Blue and OPFOR decisions across all five domains simultaneously.
On the land domain, manoeuvre elements compete for terrain, lines of communication, and key infrastructure. Force ratios, logistics state, terrain effects, and weather all influence engagement outcomes. OPFOR land manoeuvre responds to Blue dispositions – not to a pre-scripted axis of advance – which means the exercise can develop in directions a human control cell would not have anticipated or pre-authored.
The maritime domain affects land operations through amphibious access, logistic sea lines, naval fire support availability, and mine warfare effects on port throughput. In a Baltic scenario, maritime domain competition determines whether reinforcement timelines are met and whether flanking options remain available. WARG models this as a live competition rather than a scripted inject: if Blue adequately contests the maritime domain, reinforcement flows; if OPFOR achieves maritime superiority, it does not.
Air domain effects propagate into every other domain. Air superiority determines ISR availability for land commanders, suppression capability against OPFOR fires, and strategic airlift throughput. Air defence competition – OPFOR integrated air defence versus Blue fighter and suppression packages – plays out as a genuine contest driven by both sides' decisions, not a pre-determined outcome delivered on a schedule. Exercise designers set the starting air balance; the exercise determines how it evolves.
Space domain effects in WARG cover positioning, navigation, and timing (PNT) availability, satellite communications, and space-based ISR. OPFOR space domain operations – electronic warfare against GPS, spoofing of Blue navigation, and targeting of space-based assets – degrade Blue capabilities in ways that cascade across domains. Blue space domain awareness and protection decisions determine whether these degradation effects are limited or severe. Most exercises treat space as background infrastructure; WARG treats it as a contested domain where operational decisions have consequences.
Cyberspace is the fifth domain and the one most often absent from real-world wargaming due to the expertise required to adjudicate it. WARG's AI generates cyber effects – network intrusion attempts against Blue C2 infrastructure, data exfiltration affecting intelligence availability, effects against logistics management systems – calibrated to the exercise difficulty setting and responsive to Blue cyber defensive actions. Participants make cyber decisions using the same Action Card mechanism as other domains, and the AI adjudicates effects based on the current cyber posture of both sides.
Key insight: Multi-domain interaction is where WARG's generative approach produces the most training value. In a pre-authored scenario, cross-domain effects are simplified to a manageable number of authored interactions. In a WARG exercise, every decision in one domain creates conditions that the AI must account for in its adversary decisions across all other domains – producing the kind of cascading operational complexity that staff exercises are supposed to develop competency in managing.
AI adversaries and adaptive difficulty
WARG runs multiple AI adversaries providing independent strategic perspectives on the operational situation. Rather than a single monolithic OPFOR AI, the platform models independent decision-makers at different echelons – strategic, operational, and tactical – each with its own assessment of the situation and its own decision authorities. Disagreements between adversary echelons about the correct course of action produce realistic friction and coordination delays in OPFOR response, rather than the implausibly perfect coordination that single-AI OPFOR produces.
Each AI adversary evaluates the current operational situation against doctrine-consistent principles for its assigned domain and echelon, generates a candidate set of actions, selects among them based on assessed risk and opportunity, and executes. The assessment process happens on a realistic decision cycle – adversary commanders do not respond instantaneously to Blue actions. Reaction delays vary by echelon and adversary competence level, introducing realistic OPFOR decision latency that Blue commanders can exploit or fail to exploit.
Adaptive difficulty prevents pattern memorisation across exercises. The AI adversary calibration adjusts continuously based on measured Blue force performance: decision tempo, domain synchronisation effectiveness, and tactical outcomes. An adversary that consistently fails to challenge an experienced staff is not providing training value; an adversary that produces immediate decision failure in every exchange is not either. WARG maintains the adversary at the threshold of Blue capability – the zone where decisions are genuinely difficult and their consequences are instructive.
Calibration variables include adversary reaction time across domains, initiative level (whether OPFOR seizes opportunities proactively or responds reactively), combined-arms coordination quality, intelligence discipline, and the rate at which OPFOR adapts to observed Blue tactics. An adversary that never adjusts its approach provides a fixed target; an adversary that adjusts too rapidly is unrealistically prescient. WARG's calibration maintains adversary adaptation within the range consistent with realistic organisational decision-making.
Action cards and the natural language chat interface
Participants interact with the WARG exercise through two mechanisms: Action Cards and the natural language chat interface. Action Cards are structured decision formats covering each domain's action types – a land Action Card might represent a manoeuvre order, a fire mission, a request for ISR tasking, or a sustainment decision. Cards are designed for rapid deployment: a participant identifies the decision required, selects the appropriate card type, fills in the key parameters, and submits. The AI adjudicates the effect within seconds and updates the operational picture across all affected domains.
The structured card format serves a dual purpose. It constrains decision inputs to operationally meaningful actions within the participant's role authority – preventing exercises from becoming unconstrained free-for-alls – while providing the AI with consistent, parseable decision data that enables high-quality adjudication and analysis. The card set covers all five domains and is extensible for exercise-specific capabilities or constraints.
The natural language chat interface provides a different interaction channel: participants can query WARG in plain language about the operational situation, request clarification on adversary actions, ask for doctrinal guidance before committing to a decision, or request a domain-specific briefing update. The chat interface is not a decision submission mechanism – it is a coaching and situational awareness tool. It allows junior participants to develop their analytical approach by asking questions before acting, and provides exercise staff with visibility into participant reasoning before observing the decision outcome.
Real-time move-by-move AI analysis appears in the exercise staff view after each significant decision sequence. Staff can observe the AI's assessment of each Blue decision – what the adversary interpreted it as signalling, which response options the adversary considered, and what the projected effects are across domains. This continuous analysis supports instructor intervention decisions: when to allow a developing situation to play out for learning value, and when to inject a remediation discussion before consequences compound.
NATO brigade staff exercise: baltic defence scenario
A practical illustration of WARG's application is a NATO brigade-level staff exercise structured around a Baltic defence scenario. The operational context: a mechanised brigade defending assigned battle positions against a peer adversary with significant air defence, electronic warfare, and cyber capability, in a joint operational environment with maritime and air domain competition running in parallel.
Exercise setup using WARG begins with scenario seeding: the exercise director provides the operational context – area of operations, Blue and OPFOR ORBATs, command relationships, and the specific training objectives for the staff exercise. WARG's scenario generation engine produces the initial scenario within minutes, including adversary initial dispositions, assumed intelligence preparation of the battlefield, and the opening operational situation for both sides. No pre-authoring of decision branches is required.
Staff participants are assigned to roles spanning the brigade's functional areas: manoeuvre, fires, sustainment, intelligence, air liaison, and the cyberspace and space functional area coordinator positions that multi-domain operations require. Each role group receives its domain-appropriate operational picture and decision authority. A maritime coordination element representing the joint force's maritime component is included as a separate participant group, establishing the coalition coordination challenge from the outset.
The exercise runs in turns representing operational planning cycles rather than real time. Each turn, participant groups submit their decisions via Action Cards across all relevant domains. WARG adjudicates simultaneously, applies cross-domain interaction effects, advances the adversary decision cycle, and presents the updated operational picture. The AI analysis log shows the brigade's intelligence officer that OPFOR's reaction to the fires decision was to reposition air defence assets – a consequence the pre-authored inject sequence would not have generated.
Key insight: In a WARG Baltic exercise, the most instructive moments typically arise from cross-domain interactions that no exercise designer anticipated. A brigade staff that prioritises cyber defensive actions early in the exercise discovers it has degraded OPFOR targeting capability for the subsequent land engagement – a consequence that emerges from genuine multi-domain competition rather than a scripted payoff. This kind of emergent learning cannot be authored; it can only be generated.
At exercise completion, WARG produces its post-exercise report covering the full decision timeline, domain-by-domain performance assessment, cross-domain coordination effectiveness, and prioritised learning recommendations. The brigade staff's after-action review is structured around this report: the instructor uses the AI analysis as the factual baseline for the discussion, applying professional judgment to contextualise findings within the broader operational and doctrinal framework. The AAR is conducted on the day of the exercise rather than three days later.
Running a WARG exercise: step by step
Setting up and executing a WARG exercise follows a consistent process applicable to exercises of different scale and complexity.
Step 1 – Define the operational context and objectives. Input the area of operations, force ORBATs, command relationships, and training objectives. WARG uses these as constraints for scenario generation. This step replaces weeks of scenario authoring with a structured inputs session typically completed in a few hours.
Step 2 – Configure participants and role assignments. Assign exercise participants to command roles across the relevant domains. Add coalition partner groups with independent information-sharing restrictions if coalition coordination is a training objective.
Step 3 – Set difficulty baseline and adaptive parameters. Select the starting difficulty tier and configure which adaptive parameters WARG may adjust during the exercise. Exercise staff can lock specific parameters if the training design requires fixed conditions for particular exercise phases.
Step 4 – Execute using Action Cards and the chat interface. Participants submit decisions via Action Cards; use the natural language chat to request situational updates, doctrinal guidance, or pre-decision analysis. The AI adjudicates and updates the operational picture in real time.
Step 5 – Monitor real-time AI analysis. Exercise staff review move-by-move AI analysis throughout the exercise. Use this to identify emerging training opportunities and decide whether to intervene or allow consequences to develop.
Step 6 – Conduct the AAR using the generated report. Use WARG's post-exercise analysis report as the AAR baseline. Instructor supplements AI-generated findings with professional judgment and operational context. Debrief on the day of the exercise.
Step 7 – Seed follow-on exercises targeting identified gaps. Map learning recommendations to follow-on exercise objectives. Generate the next exercise scenario seeded specifically to challenge the gaps identified – creating a deliberate training progression.
Frequently asked questions
+How many players can participate in a WARG exercise simultaneously?
WARG supports joint multi-force exercises with multiple simultaneous participant groups representing different commands, services, or coalition partners. Each group interacts with the shared AI-generated operational environment from its own command perspective. The platform is designed for brigade-level and above staff exercises – typically ranging from small staff cells of 4–6 to full joint task force exercises involving dozens of participants across multiple locations.
+How are AI adversaries calibrated to the experience level of participants?
WARG's adaptive difficulty engine continuously measures player decision quality, tempo of play, and tactical outcomes, then adjusts AI adversary parameters accordingly. Calibration variables include adversary reaction time, initiative level, combined-arms coordination, and cyber and space domain activity intensity. For first-time users, adversaries begin at a conservative baseline and escalate as players demonstrate competence. Exercise staff can also manually set a starting difficulty tier and override adaptive adjustments for specific exercise phases.
+Can existing training scenarios be imported into WARG?
WARG accepts scenario seeds – structured descriptions of the operational context, force ORBATs, area of operations, and exercise objectives – which the AI uses as constraints when generating the full scenario. Existing scenario documentation such as OPORD fragments, exercise directives, and ORBAT tables can be provided as natural language input via the chat interface or as structured data. The platform does not import proprietary file formats from other simulation tools, but the seeding process typically takes less time than configuring a traditional simulator.
+What analytics does WARG provide to training staff after an exercise?
WARG generates a real-time move-by-move analysis log throughout the exercise and a comprehensive post-exercise report covering: a chronological decision timeline annotated with AI commentary; domain-by-domain performance assessment; coordination effectiveness metrics across domains; and prioritised learning recommendations mapped to training objectives. The report is available immediately at exercise completion, eliminating the multi-day delay of traditional AAR production.
+How does WARG prevent pattern memorization across repeated exercises?
Because WARG scenarios are generated by AI rather than pre-authored, no two exercises are structurally identical even when the same operational context is used as the seed. The AI scenario generator varies adversary dispositions, axis of advance, supporting fires timing, cyber intrusion vectors, space asset availability, and the sequence of decision injection points. Players cannot memorise the scenario because the scenario does not exist until the exercise begins – the fundamental difference from library-based scenario tools where familiarity degrades training value after the first few repetitions.
WARG is part of Corvus Intelligence's portfolio of AI-powered wargaming and training tools built for NATO and allied military organisations. The platform is designed to reduce exercise preparation overhead while simultaneously increasing training quality – fewer resources spent on scenario authoring, more on actual training delivery.
Related reading: For the technical architecture underlying multi-domain simulation environments, see Military Training Simulation Software: Architecture and Key Components. For AI-driven OPFOR design principles applicable across wargaming and simulation contexts, see AI OpFor in Wargaming and Training Simulation. For the after-action review process that WARG automates, see After-Action Review Software: Design and Implementation.