AI Agent Use Case - “Operation Helping Hand”

AI-Powered Mission Planning for Air Refueling Operations

Project Overview

Developed an AI-assisted mission planning system for Air Force tanker operations that reduces decision-making time from 28-98 minutes to 6-12 minutes while maintaining operational accuracy and enabling better-informed strategic decisions.

The Challenge

Air Force Tanker "Barrel Masters" face enormous cognitive burden allocating tanker assets to support refueling missions. The manual process involves complex calculations across multiple systems, spreadsheet management, and what operators call the "Tetris game" of constantly shifting assets to support high-priority missions.

Operational Context:

• Tanker assets are constantly overbooked due to high demand

• Missions require analysis of distance, availability, ongoing commitments

• High-priority missions arrive with short notice, requiring rapid reallocation

• Equipment failures and schedule delays create cascading impacts

• Legacy processes involve 56-76 manual steps taking 28-98 minutes per allocation

Critical Pain Points:

• 6+ hours daily spent on manual data entry and spreadsheet updates

• Single point of failure (one "owner" per spreadsheet)

• No real-time data synchronization across systems

• Steep learning curve for new personnel

• High cognitive burden analyzing which units best suited for missions

• Limited time for strategic validation and decision-making

The Solution

A multi-agent AI workflow that analyzes mission requirements, generates courses of action, assesses resources, evaluates risks, and presents recommendations with transparent reasoning—enabling Barrel Masters to focus on strategic decision-making rather than manual data processing.

Workflow Architecture:

Agent 1: Mission Requirements Analysis

• Processes incoming refueling requests

• Analyzes AR track locations ("gas stations in the air")

• Identifies mission criticality and timing constraints

• Extracts key operational parameters

Agent 2: Asset Allocation Planning

• Evaluates available tanker units and current commitments

• Calculates distance and feasibility for each potential asset

• Considers ongoing missions and maintenance schedules

• Generates multiple courses of action (COAs)

Agent 3: Resource Optimization

• Analyzes efficiency vs. effectiveness trade-offs

• Identifies pre-positioning opportunities and risks

• Calculates optimal asset utilization across multiple missions

• Flags potential conflicts or resource constraints

Agent 4: Risk Assessment & Recommendation

• Evaluates operational risks for each COA

• Provides confidence levels and reasoning transparency

• Highlights assumptions and limitations

• Presents recommendations with clear decision rationale

Agent 5: Evaluation & Validation

• Checks operational feasibility against military constraints

• Validates calculations and assumptions

• Ensures compliance with operational procedures

• Enables human override and customization based on knowledge not yet in system

UX Design Integration

• Visual Interface: Map visualization showing tanker routes and AR track distances

• Dynamic Updates: Real-time display of decision impacts as users review COAs

• Customization Features: Allows Barrel Masters to modify calculations based on upcoming high-priority missions or other contextual knowledge

• Transparent Reasoning: Shows AI logic and assumptions for each recommendation

• Human-in-the-Loop: Clear decision points where human expertise overrides AI suggestions

Technical Implementation

• Platform: Cassidy AI with custom workflow design

• Domain Knowledge: Military terminology, operational constraints, and asset allocation principles built into system instructions

• Personalization: Context-aware AI that understands Barrel Master role and decision-making patterns

• Integration: Designed to work with existing military data systems and workflows

Results & Impact

• Time Reduction: 28-98 minutes → 6-12 minutes per allocation (80-90% reduction)

• Step Reduction: 56-76 manual steps → 9-16 steps

• Cognitive Load: Dramatically reduced manual calculation burden

• Strategic Focus: Freed Barrel Masters to focus on validation and strategic decision-making

• Scalability: Consistent performance across simple and complex mission scenarios

• Man-Hour Savings: Estimated 1560+ man-hours saved annually per Barrel Master

Validation & Testing

• User Feedback: Iterative design based on actual Barrel Master input

• Beta Testing: Currently in operational evaluation with real Air Refueling missions

• Customer Journey Mapping: Documented and validated with 13-year industry veterans

• Chain of Command Approval: Material briefed to TRANSCOM and Secretary of Defense

Key Design Principles Applied

• Shared Responsibility: AI generates recommendations; humans make final decisions

• Transparency: Clear reasoning and assumptions visible at all decision points

• Appropriate Trust Calibration: System shows confidence levels and limitations

• Human Agency Preservation: Override capabilities and customization options

• Domain Expertise Integration: Military context and terminology built into AI understanding

Skills Demonstrated

• High-stakes AI system design for government/defense applications

• Multi-agent workflow architecture for complex operational environments

• User research and customer journey mapping in military contexts

• UX design for expert users with deep domain knowledge

• Human-AI collaboration system design with appropriate oversight

• Integration of AI capabilities with existing operational workflows

• Systematic evaluation and iterative refinement based on user feedback

Broader Applications

The same architectural principles apply to:

• Intelligence analysis workflows (data processing → pattern identification → hypothesis generation → analyst review)

• Mission planning across other military domains (requirements → options → assessment → recommendation)

• Resource allocation in other high-stakes contexts (healthcare, emergency response, logistics)

Future Development

• Integration with live military data systems

• Expanded evaluation metrics (Time on Track comparisons, efficiency vs. effectiveness analysis)

• Enhanced audit capabilities for workflow optimization

• Machine learning integration for continuous improvement based on actual mission outcomes

• Expansion to other Air Force operational domains (Airlift, Coronets, etc.)

Cross-Project Insights

Both projects demonstrate the same fundamental principles applied in radically different contexts:

Universal Design Patterns:

• Specialized agents with clear roles

• Systematic handoffs maintaining context

• Transparent reasoning and decision logic

• Human oversight at critical points

• Evaluation frameworks ensuring quality

• Voice/domain expertise preservation

Context-Specific Adaptation:

• Bible education: Theological accuracy, nurturing voice, accessibility

• Mission planning: Operational feasibility, resource optimization, risk mitigation

The Core Insight: Reliable AI systems in any high-stakes context require the same architectural approach—only the evaluation criteria and domain expertise change.