AI agents are more complex than your average app. They include not just application functions but also automation, data handling, APIs, and AI models. Because of this, their nonfunctional requirements (NFRs) need to be more layered and detailed. These NFRs help ensure the system runs smoothly, securely, and responsibly. They also guide developers and teams on what to prioritize at different stages, from development to release.

Understanding Nonfunctional Requirements for AI Agents

In traditional software, NFRs cover things like speed, security, and stability. For AI agents, these requirements are similar but expanded. Besides performance and security, teams need to focus on data quality, bias, fairness, and ethical behavior. Since AI agents interpret natural language, they must also be evaluated for explainability and bias. These qualities aren’t just nice to have—they’re critical for building trustworthy AI systems.

Agile teams often write user stories to describe what the AI should do, focusing on functional goals. But they also need to specify nonfunctional criteria. For example, an AI’s response time, accuracy, or fairness must be measurable. These criteria can be embedded into user stories as acceptance tests, helping teams verify that the AI meets necessary standards before deployment.

Key Areas for Nonfunctional Requirements in AI

One major area is ethics and fairness. AI models, especially large language models, interpret and generate responses in natural language. Teams must check whether these responses are responsible, unbiased, and explainable. This can be tricky, but formal verification methods are increasingly used to turn NFRs into quantifiable, testable requirements. For instance, an NFR might specify that 80% of responses should be explainable according to expert review, or that bias detection tools should flag less than 5% of outputs for bias.

Another critical area is data management. AI systems depend heavily on data quality and governance. NFRs here include measures for bias detection, data privacy, and model maintenance. They specify acceptable levels of bias, data anonymization standards, and how often models should be retrained or audited to prevent drift. These requirements help ensure AI systems remain fair, accurate, and compliant over time.

Security and compliance are also vital. Since AI systems often handle sensitive data, NFRs must specify security measures like encryption, input sanitization, and protection against prompt injections. They also cover regulatory compliance, requiring AI to prevent misuse and protect user privacy. Using embedded security tools at runtime is common practice to meet these standards consistently.

Performance, Scalability, and Maintainability

Performance metrics for AI agents include response times, throughput, and scalability. For example, an NFR might state that 98% of responses should be within one second, or that the system can support 100 concurrent users without performance drops. Scalability requirements ensure the system can handle sudden spikes—sometimes 10 times normal load—without losing speed or accuracy.

Maintaining an AI system is just as important as building it. NFRs should specify how easy it is to update models, monitor system health, and troubleshoot issues. Observability tools help teams track performance, detect failures, and quickly respond to problems. These requirements link development work with operational needs, ensuring the AI remains reliable over time.

In practice, balancing these requirements is about knowing what can be automated and what still needs human judgment. Experts like Jonathan Zaleski say that security, compliance, and scalability are often best enforced by machines, while user experience and aesthetic qualities still benefit from human oversight. The future of AI development is a hybrid approach, combining machine enforcement of objective standards with human intuition for the emergent, intuitive aspects of AI systems.

In summary, nonfunctional requirements for AI agents are about more than just speed and security. They include ethical considerations, data governance, performance benchmarks, and operational health. Clear, measurable NFRs help teams build AI systems that are trustworthy, efficient, and ready for real-world use. As AI continues to evolve, so will the need for comprehensive, well-defined nonfunctional requirements that keep pace with technological and societal expectations.

Inspired by

• https://www.infoworld.com/article/4061123/how-to-write-nonfunctional-requirements-for-ai-agents.html

Sources

• gmpg.org
• starcio.com
• starcio.com
• starcio.com
• starcio.com