Agentic-AI, Schema-Driven Decision Management for Auditable Studies and Acquisition Decisions

Agentic-AI, Schema-Driven Decision Management for Auditable Studies and Acquisition Decisions is sponsored by U.S. Army Office of Small Business Programs. This opportunity seeks to develop a decision-as-a-program capability that turns complex engineering/acquisition questions into structured, auditable Decision Packages.

Agentic-AI, Schema-Driven Decision Management for Auditable Studies and Acquisition Decisions – Army SBIR|STTR Program Artificial Intelligence/Machine Learning, Army SBIR | Army STTR, Phase I Agentic-AI, Schema-Driven Decision Management for Auditable Studies and Acquisition Decisions Topic Number: ARM26BX06-NV012 (SBIR); ARM26TX06-NV003 (STTR) Navigate to DSIP to apply today.

​ ​ Prove a decision-as-a-program capability that turns complex engineering/acquisition questions into structured, auditable Decision Packages – faster, with higher confidence. This opportunity will be open for both SBIR and STTR participation.

​ SBIR topic number: ARM26BX06-NV012 ​ STTR topic number: ARM26TX06-NV003 ​ ​ ​ ​ This topic will prototype and demonstrate a schema-driven Decision Management (DM) foundation plus a governed agentic AI layer that turns messy decision requests into auditable, signer-ready Decision Packages.

The focus is proof by demonstration (not a full product build) to show the approach works for point trade studies and long-horizon acquisition decision programs with traceability, robustness, and human control. AI can process vast amounts of structured and unstructured data from Digital Engineering (DE) ecosystems, including digital threads and digital twins, enabling comprehensive analysis across the entire product lifecycle.

Interoperability across models is achieved as AI integrates data from various DE tools (e.g., SysML 2. 0, UML, etc.) and provides a unified view of system models, enabling cross-functional teams to collaborate effectively. ​ The first objective is to demonstrate a unified representation that supports both (a) single-episode trade studies and (b) multi-episode “decision programs” with refresh cycles.

This includes implementing context engineering specifically for the ground vehicle domain through knowledge graphs and semantic layers that capture domain-specific relationships, constraints, and requirements. ​ The second objective is to implement agentic capabilities that produce structured artifacts and reproducible outputs, while maintaining human control and predictable behavior per established human-AI interaction guidance.

AI will automate the validation of technical documents, models, and outputs, ensuring compliance with standards and requirements throughout the decision-making process. ​ The third objective is to extend the schema so bias checks, risks, and assumptions are explicit, testable objects – so decision readiness can be assessed before sign-off and refreshed responsibly when conditions change.

The system will leverage semantic understanding to maintain consistency across digital engineering artifacts and enable traceable decision rationale. ​ ​ ​ ​ The Phase 1 Desired Outcomes are as follows: ​ A decision schema that makes objectives, options, constraints, assumptions, risks, and bias checks first-class objects.

​ Agentic AI that: performs structured elicitation into the formal model, then generates decision workflow plans, and executes reproducible evaluation runs with clear “what flips the decision” logic. ​ Two end-to-end demonstrations showing the approach scales from point-in-time trade studies to long-horizon program decisions with refresh cycles as evidence and assumptions change.

​ ​​The DoW currently has multiple contractors developing AI/LLM tools for core warfighter purposes. However, little effort has been expended towards an AIgentics decision intelligence capability that supports accelerated commercial acquisition or longer lead traditional development.

The DoW is agnostic as to a developed AI commercial package that fulfills adding AIgentics to current AI/LLM for Decision Intelligence for continuous acquisition. During Phase II it is expected that the successful contractor will develop a combined AIgentics and AI/LLM decision intelligence workflow capability with two cases provided from PAE Maneuver Ground and PAE Fires.

This software platform needs to be modeled and developed as a commercial open-source platform that both the DoW and commercial enterprises would use for their Product Design & Development (PDD) and acquisition. The current COTS application, DAOSoft, provides an open capability that employs an advanced structured decision/trade study capability with a partially developed AI/LLM decision intelligence workflow.

The Army is looking for any and all ideas that can extend or replace current acquisition capabilities and is agnostic to all current solutions. The successful Phase II prototype will be immediately useful, but it is also expected that the contractor would potentially execute follow-on Phase III contracts for individual customer installation.

This would be similar to the current best practice deployment of large ERP systems from SAP and Oracle. ​ ​​Potential commercial use cases include the vehicle design process for the automotive industry. The workflow can be relatively similar to the vehicle design process for the Army with some different considerations.

​ ​​​​For more information, and to submit your full proposal package, visit the DSIP Portal . ​ SBIR|STTR Help Desk: usarmy. sbirsttr@army.

mil https://saemobilus. sae. org/papers/concept-execution-ai-agentic-decision-intelligence-framework-product-planning-concept-development-2025-01-0455 Keywords: Agentic AI; Digital Engineering; Schema-driven Decision Management; Acquisition Decisions; AI LLM Navigate to DSIP to apply today.

​ ​ Prove a decision-as-a-program capability that turns complex engineering/acquisition questions into structured, auditable Decision Packages – faster, with higher confidence. This opportunity will be open for both SBIR and STTR participation.

​ SBIR topic number: ARM26BX06-NV012 ​ STTR topic number: ARM26TX06-NV003 ​ ​ ​ ​ This topic will prototype and demonstrate a schema-driven Decision Management (DM) foundation plus a governed agentic AI layer that turns messy decision requests into auditable, signer-ready Decision Packages.

The focus is proof by demonstration (not a full product build) to show the approach works for point trade studies and long-horizon acquisition decision programs with traceability, robustness, and human control. AI can process vast amounts of structured and unstructured data from Digital Engineering (DE) ecosystems, including digital threads and digital twins, enabling comprehensive analysis across the entire product lifecycle.

Interoperability across models is achieved as AI integrates data from various DE tools (e.g., SysML 2. 0, UML, etc.) and provides a unified view of system models, enabling cross-functional teams to collaborate effectively. ​ The first objective is to demonstrate a unified representation that supports both (a) single-episode trade studies and (b) multi-episode “decision programs” with refresh cycles.

This includes implementing context engineering specifically for the ground vehicle domain through knowledge graphs and semantic layers that capture domain-specific relationships, constraints, and requirements. ​ The second objective is to implement agentic capabilities that produce structured artifacts and reproducible outputs, while maintaining human control and predictable behavior per established human-AI interaction guidance.

AI will automate the validation of technical documents, models, and outputs, ensuring compliance with standards and requirements throughout the decision-making process. ​ The third objective is to extend the schema so bias checks, risks, and assumptions are explicit, testable objects – so decision readiness can be assessed before sign-off and refreshed responsibly when conditions change.

The system will leverage semantic understanding to maintain consistency across digital engineering artifacts and enable traceable decision rationale. ​ ​ ​ ​ The Phase 1 Desired Outcomes are as follows: ​ A decision schema that makes objectives, options, constraints, assumptions, risks, and bias checks first-class objects.

​ Agentic AI that: performs structured elicitation into the formal model, then generates decision workflow plans, and executes reproducible evaluation runs with clear “what flips the decision” logic. ​ Two end-to-end demonstrations showing the approach scales from point-in-time trade studies to long-horizon program decisions with refresh cycles as evidence and assumptions change.

​ ​​The DoW currently has multiple contractors developing AI/LLM tools for core warfighter purposes. However, little effort has been expended towards an AIgentics decision intelligence capability that supports accelerated commercial acquisition or longer lead traditional development.

The DoW is agnostic as to a developed AI commercial package that fulfills adding AIgentics to current AI/LLM for Decision Intelligence for continuous acquisition. During Phase II it is expected that the successful contractor will develop a combined AIgentics and AI/LLM decision intelligence workflow capability with two cases provided from PAE Maneuver Ground and PAE Fires.

This software platform needs to be modeled and developed as a commercial open-source platform that both the DoW and commercial enterprises would use for their Product Design & Development (PDD) and acquisition. The current COTS application, DAOSoft, provides an open capability that employs an advanced structured decision/trade study capability with a partially developed AI/LLM decision intelligence workflow.

The Army is looking for any and all ideas that can extend or replace current acquisition capabilities and is agnostic to all current solutions. The successful Phase II prototype will be immediately useful, but it is also expected that the contractor would potentially execute follow-on Phase III contracts for individual customer installation.

This would be similar to the current best practice deployment of large ERP systems from SAP and Oracle. ​ ​​Potential commercial use cases include the vehicle design process for the automotive industry. The workflow can be relatively similar to the vehicle design process for the Army with some different considerations.

​ ​​​​For more information, and to submit your full proposal package, visit the DSIP Portal . ​ SBIR|STTR Help Desk: usarmy. sbirsttr@army.

mil https://saemobilus. sae. org/papers/concept-execution-ai-agentic-decision-intelligence-framework-product-planning-concept-development-2025-01-0455 Keywords: Agentic AI; Digital Engineering; Schema-driven Decision Management; Acquisition Decisions; AI LLM Assistant Secretary of the Army for Acquisition, Logistics, and Technology ASA(ALT) releases contract opportunities on an ad-hoc basis to meet Army research and development needs.

Army Futures Command (AFC) releases topics during three specific solicitation periods throughout the fiscal year to address the Army’s current and anticipated war-fighting technology needs. Army STTR follows AFC’s topic release schedule but partners with a university, federally funded research and development center, or a qualified non-profit research institution as part of their contract.

Is the opportunity to establish the scientific, technical, commercial merit and feasibility of your proposed innovation. Is focused on the development, demonstration and delivery of your innovation from Phase I. Represents the commercialization phase of the program in which the company can market their products or services developed in Phase II, either to the government or in the commercial sector.

Allows small businesses to submit to Direct to Phase II applications if they performed the Phase I research through other funding sources. Provides funding to projects that require additional funding during their open Phase II contract. A Phase II Awardee may receive one additional, sequential Phase II award to continue the work of an initial Phase II award.

The sequential Phase II award has the same guideline amounts and limits as an initial Phase II award.

Artificial Intelligence/Machine Learning (supply chain management, logistics coordination, target identifications and simulation) Advanced Materials and Manufacturing (additive manufacturing) Autonomy (unmanned systems, drones, ground vehicle capabilities) Chemical and Biological (detection, defense) Cyber (biometric authentication, secure communications) Electronics (microelectronics, Very-Large-Scale Integration (VLSI)) Electronic Warfare (jamming, spoofing) Human Performance (wearables) Immersive (augmented reality, virtual reality, mixed reality) Network Technologies (antennas, radio frequency, communications systems) Position, Navigation, and Timing (GPS) Power (batteries, generators) Software Modernization (high performance computing, data management and visualization) Sensors (infrared sensing) Weapons Systems (hypersonics, munitions and projectiles, directed energy)