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Artificial Intelligence Engineering

The Software Engineering Institute (SEI) is advancing the Department of Defense’s vision of creating viable, trusted, and extensible artificial intelligence (AI) systems by leading the development of a professional AI Engineering discipline.

The need for a mature engineering discipline to guide AI capabilities is urgent. AI Engineering—an emergent discipline focused on applying AI in real-world contexts—accelerates the development of capabilities and maturation of individual tools, particularly for such high-stakes scenarios as responding to national security threats and military operations. To realize the benefits of AI for such scenarios, we must successfully meet the challenges unique to AI systems. After all, while the capability to develop AI systems has risen due to available computing power and datasets, these systems often work only in controlled environments and are difficult to replicate, verify, and validate in the real world. For example, while an uncrewed aerial vehicle (UAV) functions well on a test range on a clear day, how can it be designed to function just as effectively during a windstorm on a battlefield? AI Engineering aims to provide a framework and tools to proactively design AI systems to function in environments characterized by high degrees of complexity, ambiguity, and dynamism.

By leading the development of the discipline of AI Engineering, the SEI aims to equip practitioners to develop systems across the enterprise-to-edge spectrum, to anticipate requirements in changing operational environments and conditions, and to ensure human needs are translated into AI that warfighters and operators can trust.

Developing the Discipline of AI Engineering

AI Engineering is taking shape as a discipline already across different organizations and institutions. We at the SEI see ourselves not only a source of AI Engineering expertise, but also as conveners and catalysts, bringing together people and ideas to share the lessons learned, the techniques developed, and the discoveries made.

With funding and guidance from the U.S. Office of the Director of National Intelligence (ODNI), the SEI is leading a national initiative to advance the discipline of AI engineering that aligns with the DoD’s vision of creating viable, trusted, and extensible AI systems.

AI Engineering Supports Mission Outcomes

AI Engineering is a field of research and practice that combines the principles of systems engineering, software engineering, computer science, and human-centered design to create AI systems in accordance with human needs for mission outcomes. Through conversations with partners, we’ve developed three pillars to guide our approach to AI Engineering.

Human-centered AI

Key to the implementation of AI in context is a deep understanding of the people who will use the technology. This pillar examines how AI systems are designed to align with humans, their behaviors, and their values.


Read more about human-centered AI.

Scalable AI

Effective AI systems require large investments of time and money to develop. This pillar examines how AI infrastructure, data, and models may be reused across problem domains and deployments.


Read more about scalable AI.

Robust and Secure AI

One of the biggest challenges facing the broad adoption of AI technologies and systems is knowing that AI systems will work as expected when they are deployed outside of closely controlled development, laboratory, and test environments. This pillar examines how we develop and test resilient AI systems.


Read more about robust and secure AI.

Selected AI Engineering Resources

The SEI works to publish information to advance the field of AI and to highlight the work of other researchers and partners who are developing secure and robust AI.

  • The SEI report AI Engineering for Defense and National Security is a product of the first-ever workshop on AI Engineering that brought together thought leaders in defense and national security, industry, and academia. This workshop was a key milestone in developing the pillars of AI Engineering.
  • The SEI short paper AI Engineering: 11 Foundational Practices offers recommendations to help organizations build, acquire, and integrate artificial intelligence capabilities into business and mission systems.
  • In 2022, the SEI hosted the AAAI Spring Symposium on AI Engineering alongside co-organizers from Duke University, SRI International, and MIT Lincoln Lab. The symposium focused on human-centered, scalable, and robust and secure AI, with the goal of further evolving the state of the art; gathering lessons learned, best practices, workforce development needs; and fostering critical relationships.

What We Offer

The Latest from the SEI Blog

Protecting AI from the Outside In: The Case for Coordinated Vulnerability Disclosure

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This post highlights lessons learned from applying the coordinated vulnerability disclosure (CVD) process to reported vulnerabilities in AI and ML systems.

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Introducing MLTE: A Systems Approach to Machine Learning Test and Evaluation

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Machine learning systems are notoriously difficult to test. This post introduces Machine Learning Test and Evaluation (MLTE), a new process and tool to mitigate this problem and create safer, more reliable systems.

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Latest from the Digital Library

AI Hygiene Starts with Models and Data Loaders

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This paper places a call to action for traditional cybersecurity tools and techniques to be applied to artificial intelligence (AI) for improving the cybersecurity of AI systems.

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Improving Machine Learning Test and Evaluation with MLTE

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Machine learning (ML) models commonly experience issues when integrated into production systems. MLTE provides a process and infrastructure for ML test and evaluation.

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Explore Our Artificial Intelligence Engineering Projects

Artificial Intelligence Engineering Topic Page Looking Ahead

Our Vision for the Future of AI Engineering

Bolstered by our expertise in developing applications for AI, the SEI is leading the way for developing scalable, robust and secure, and human-centered AI systems—and supporting, expanding, and evolving those systems into the future.

Join us in advancing the AI Engineering discipline.