Aircraft Maintenance, Repair, and Overhaul (MRO) operations are highly complex, involving coordination among multiple stakeholders including airlines, MRO providers, OEMs, and regulatory authorities. A significant challenge in this space is managing unplanned events such as Aircraft on Ground (AOG) conditions, where delays can lead to major financial losses to airlines and safety risks. Engineers must quickly diagnose the damage, evaluate compliance against regulatory limits, coordinate with OEMs, and make critical decisions—all while navigating a fragmented ecosystem of disconnected systems, diverse document types, and time-sensitive processes.
This paper presents a real-world, intelligent MRO solution that addresses these challenges through the use of Agentic AI and context engineering. The system is designed to automate and augment key MRO workflows such as damage detection, repair pathway selection, compliance verification, and supplier coordination. At its core, the solution is powered by a set of autonomous agents—each responsible for specific tasks like interpreting repair manuals, evaluating damage severity, or communicating with OEM portals.
A key innovation of this system is its use of context engineering, which enables agents to share a unified, real-time view of the aircraft condition, document references, decisions made, and deadlines involved. This shared memory—dynamically updated using modern data stores and retrieval systems—ensures that agents and human experts operate with full situational awareness.
The solution facilitates measurable improvements in reducing aircraft downtime, speeding up OEM coordination, and ensuring real-time continuous regulatory compliance. Engineers can take faster, more informed decisions with confidence, while human-in-the-loop oversight was preserved for critical steps such as compliance sign-off and final approvals.
Overall, this intelligent MRO system transforms static, document-heavy processes into a dynamic, context-aware workflow. It brings together agent collaboration, regulatory alignment, and real-time information flow to solve one of the most pressing operational problems in aviation today. By improving inspection-to-repair cycles, enhancing SLA adherence, and enabling traceable, data-driven decision-making, this work lays the foundation for the next generation of digital MRO ecosystems that are efficient, safe, and scalable.