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AI for COA generation is decision-support software that proposes, compares, and risk-ranks courses of action during military planning, surfacing assumptions and risk for the commander, who stays in command of the decision.
Why it matters to your mission. Under planning time pressure, a staff can only hand-build and compare a few courses of action, and the assumptions behind them stay buried. Software that proposes, compares, and risk-ranks COAs surfaces those assumptions and risks for the commander. The outcome is faster planning across a wider option space, with the commander firmly in command of the decision.
AI for COA generation is decision-support software that proposes, compares, and risk-ranks courses of action during military planning, surfacing assumptions and risk for the commander, who stays in command of the decision.
A course of action is a potential way to accomplish the mission. Developing, analyzing, wargaming, and comparing several COAs is the core analytic work of joint and service planning, and it is where staffs run out of time. The hard part is not generating options; it is comparing them honestly, showing what each one assumes, where it is exposed, and how doctrine applies, fast enough to brief a decision. AI can accelerate the mechanical work of COA development and wargaming without taking the decision away from the commander, provided every proposed option and every risk judgment traces back to doctrine and to the evidence behind it.
It is software that reads the planning picture and drafts candidate courses of action for the staff to refine. It does not pick the plan. In the joint planning process laid out in Joint Publication 5-0, Joint Planning, COA development, COA analysis and wargaming, COA comparison, and COA approval are distinct steps the planning staff owns, and the commander approves the selected COA. AI speeds the drafting and the side-by-side comparison; the staff stays accountable for the output and the commander stays accountable for the decision.
The value is not a single "best" answer. It is coverage. A staff working by hand develops two or three COAs because that is what the clock allows. Software can draft a wider set of distinct options against the same mission, commander's intent, and constraints, then keep each one internally consistent as the planning picture changes. The staff spends its limited time judging options rather than hand-building them.
Wargaming tests a COA against likely enemy actions and conditions to expose where it breaks. AI can run candidate COAs against a range of adversary responses and environmental conditions, flag branches and sequels, and surface the decision points and assumptions a COA depends on. That gives the staff more reps in less time and a record of what was tested against what.
The commander and staff still adjudicate the wargame. They set the scenario, define the assumptions, decide which adversary behaviors are plausible, and choose what to carry forward into COA comparison. The software widens the space the staff can explore and documents the reasoning; it does not replace the staff's judgment about which outcomes matter.
It pairs pattern-based models with explicit, rule-based doctrine so a ranking can be explained, not just scored. The symbolic layer encodes doctrine, commander's intent, and evaluation criteria as rules; the learned layer handles pattern recognition over messy reporting. A COA's ranking then comes with the reasons behind it, tied to stated criteria, rather than an opaque number.
Risk is assessed against a structured method rather than an improvised scale. The Chairman of the Joint Chiefs of Staff Manual 3105.01B, Joint Risk Analysis Methodology, defines how to characterize risk by probability and consequence against a defined scale, so each COA's risk rating ties to a shared method a staff can defend in a decision brief. A ranking that cannot be explained against doctrine and a risk scale is not a ranking a commander can act on.
No. The commander stays in command of the loop. The system grounds each recommendation in doctrine and returns an auditable, traceable judgment, so when doctrine or the available evidence does not support a recommendation, it says so instead of guessing. That matches federal direction on human oversight and pre-deployment testing for high-impact AI in the Office of Management and Budget memorandum M-25-21, which directs agencies to implement minimum risk-management practices, maintain human oversight, and discontinue high-impact AI that is not performing at an appropriate level. It also follows the DoW ethical principles for artificial intelligence: Responsible, Equitable, Traceable, Reliable, and Governable.
Because a plan has to survive a decision brief and, later, a review of why the decision was made. A commander cannot stake a mission on a recommendation nobody can explain, and a staff cannot defend a COA whose risk rating came from a number no one can trace. If the tool cannot show why it ranked COA A over COA B, the staff has to redo the analysis by hand, and the tool has added risk rather than removed it.
This is also a data problem before it is a model problem. COA analysis runs on fragmented reporting pulled from many systems, and independent oversight has documented how hard that integration is in defense command and control. The U.S. Government Accountability Office, in GAO-25-106454, Defense Command and Control (2025), found that the data a decision depends on is often stove-piped across incompatible systems and standards, with intelligence data frequently not accessible, and that the department lacked a framework to guide integration. A COA tool that reasons over that data has to preserve where each input came from, or its conclusions cannot be trusted or traced.
The distinction is who owns the decision and whether the reasoning is inspectable. The table contrasts an automated "answer machine" against decision-support infrastructure built for a command setting.
| Dimension | Fully automated planning | AI-assisted COA support (decision-support) |
|---|---|---|
| Who decides | The system selects the plan | The commander approves the COA; the system informs |
| Transparency | Score or recommendation, reasoning hidden | Ranking tied to stated criteria and doctrine, inspectable |
| Doctrine | Implicit in the model | Explicit, rule-based, cited to doctrine |
| Risk rating | Opaque number | Tied to a defined method (CJCSM 3105.01B) |
| Behavior on weak evidence | Guesses to produce an answer | Abstains and says doctrine or data is insufficient |
| Provenance | Often lost | Every input traces back to its source |
| Accreditation posture | Hard to accredit a black box | Auditable, aligned to M-25-21 and DoW AI principles |
The point is not that automation is wrong. It is that a COA tool in a command setting has to be inspectable and abstainable, because the output has to be briefed, challenged, and defended.
The system informs; the commander approves the COA
Fully automated planning selects the plan and hides its reasoning; AI-assisted COA support informs the commander with an inspectable ranking tied to doctrine and a defined risk method, and abstains when evidence is weak.
The point is not that automation is wrong. A COA tool in a command setting has to be inspectable and abstainable, because the output has to be briefed, challenged, and defended.
Torch.AI builds government-owned reasoning infrastructure that complements existing planning and command systems rather than replacing them. In COA work the contribution is upstream of the ranking: making the fragmented inputs a staff reasons over trustworthy and traceable.
The reasoning stays doctrine-grounded and auditable: every input preserves its provenance, and judgments can be inspected and reversed. See how this is packaged on the Torch.AI software page.
It is software that reads the planning picture and drafts candidate courses of action for the staff to refine. It does not pick the plan. In Joint Publication 5-0, COA development, analysis and wargaming, comparison, and approval are steps the planning staff and commander own; AI speeds the drafting and comparison while the human stays accountable for the output.
AI can run candidate COAs against likely enemy actions and conditions, flag branches and sequels, and expose where an option breaks. This gives the staff more reps in less time. The commander and staff still adjudicate the wargame, set the assumptions, and choose what to carry forward.
It pairs pattern-based models with explicit, rule-based doctrine so a ranking can be explained, not just scored. Risk is assessed against a structured method such as the Joint Risk Analysis Methodology in CJCSM 3105.01B, so each COA's risk rating ties to a defined scale rather than an opaque number.
No. The commander stays in command of the loop. The system grounds each recommendation in doctrine and returns an auditable, traceable judgment, and when doctrine does not support a recommendation it says so instead of guessing. This matches federal direction on human oversight and pre-deployment testing for high-impact AI in OMB M-25-21 and the DoW principles of AI ethics.
Because a plan has to survive a decision brief and a later review. A commander cannot stake a mission on a recommendation nobody can explain, and a staff cannot defend a risk rating it cannot trace. If the tool cannot show why it ranked one COA over another, the staff has to redo the work by hand.
Fully automated planning selects the plan and hides its reasoning; AI-assisted COA support informs the commander with an inspectable ranking tied to doctrine and a defined risk method, and abstains when evidence is weak. The commander approves the COA.
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Bring a planning problem. We will scope AI course of action generation around your doctrine and data. No black box. Your planning data. Real work.
