Crafting Non-Functional Requirements for AI Agent Success

Defining Non-Functional Requirements for Advanced AI Agents

The development of artificial intelligence agents introduces a new dimension to software engineering, requiring a refined approach to defining success criteria. Traditional agile user stories typically outline “who,” “what,” and “why” from an end-user perspective, often summarized as “As a user type, I want to complete a task so that I can achieve a specific outcome.” These stories are complemented by functional acceptance criteria, which detail the user experience, business logic, and automated behaviors. However, the multifaceted nature of AI agents, which encompass application, automation, data, API, and AI components, demands a comprehensive set of non-functional requirements (NFRs) to ensure their ethical, secure, and efficient operation.

Technical leads, architects, security specialists, and DevOps engineers play a crucial role in integrating NFRs that address the system’s performance, operational robustness, and compliance mandates. For AI agents, these NFRs extend to cover critical areas such as data quality, governance, bias detection, and model maintenance. While some NFRs for AI agents mirror those for conventional applications—guiding developers on how to implement functionality and defining code review standards—an additional layer of NFRs is necessary at the feature or release level. These higher-level NFRs validate an AI agent’s readiness for deployment, establish data and AI governance policies, and set non-negotiable DevOps standards.

Jonathan Zaleski, director of technical architecture at HappyFunCorp, emphasizes the importance of distinguishing between NFRs best enforced by machines, such as security, compliance, and scalability, and those still requiring human judgment, like user experience and aesthetic performance. He highlights that the future of AI product development lies in “hybrid workflows,” where AI handles objective, measurable criteria at scale, allowing humans to focus on the emergent, intuitive aspects that shape truly meaningful experiences. This dual approach ensures both technical integrity and a refined user interaction.

Crafting Ethical and Quality-Driven NFRs

The foundation of any responsible AI agent lies in its adherence to ethical standards and the delivery of accurate, high-quality outputs. Large language models (LLMs) powering AI agents are designed to interpret natural language requests, execute actions, and provide informed recommendations. Therefore, development teams must rigorously consider non-functional acceptance criteria to validate unbiased and responsible agent behavior. This is not a trivial task, as it involves establishing quantifiable pass-fail expressions in areas that are inherently complex to define.

Grant Passmore, co-founder of Imandra, notes that agile teams often struggle to evaluate NFRs such as latency, fairness, or explainability, which might appear non-functional. However, with careful specification, these can be transformed into concrete elements within a user story, complete with clear pass-fail tests. His team utilizes formal verification to convert NFRs into mathematical functional requirements that can be definitively proven or disproven. This rigorous approach ensures that abstract ethical considerations are translated into verifiable technical specifications.

For example, ethical and fairness NFRs frequently necessitate the creation of detailed test scenarios, scaled with synthetic datasets, to evaluate the AI agent’s responses comprehensively. Explainability, a critical aspect, might be quantified by requiring that “The explanation behind responses and recommended actions should meet the explainability expectations of 80% of the subject matter expert group.” Addressing data bias involves educating development teams on various bias types and employing bias detection tools with specific acceptance metrics. Furthermore, preventing harmful responses means transforming abusive or deceptive outputs into a measurable functional metric by leveraging analytical tools to scrutinize the AI agent’s recommendations and actions.

Beyond ethical considerations, NFRs must also target an AI agent’s practical usefulness, the accuracy of its actions, and the overall quality of its responses. These NFRs should be tailored to the specific type of work the AI agent performs. For instance, the F1 score, which measures a model’s accuracy by balancing precision and recall, could be set with a minimum requirement, such as “a minimal F1 score of 0.85.” The hallucination rate, capturing instances where an AI agent produces factual errors or inaccuracies, is another crucial metric. User satisfaction scores, gathered through feedback mechanisms embedded in the agent’s human-in-the-middle interface, provide valuable qualitative data. Additionally, adversarial testing, which involves setting up datasets and automating tests designed to “break” an AI agent, helps uncover vulnerabilities and improve robustness. Josh Mason, CTO of RecordPoint, stresses that “Every AI feature must specify what acceptable performance looks like, whether it’s 90% precision for classification or relevant output from an LLM,” underscoring the need for concrete, measurable performance benchmarks.

Ensuring Security, Performance, and Operational Excellence

The integration of security, privacy, compliance, and legal considerations into AI agent development is paramount, often demanding a blend of technological capabilities and stringent requirements across user story, feature, and release levels. Given AI’s inherent non-deterministic nature, embedding technological solutions within the AI agent and its runtime environment offers continuous protection to meet evolving compliance standards.

Josh Mason of RecordPoint emphasizes that “AI systems must prevent abuse and protect sensitive data.” He offers practical advice for developing data security NFRs, such as treating prompt injection as the “new SQL injection,” necessitating runtime technology to thwart intrusions. Machine learning models require anonymized and encrypted data, making these critical feature-level NFRs before new datasets are integrated. Furthermore, LLMs need robust input sanitization, personally identifiable information (PII) redaction, and other protective guardrails to prevent manipulation through adversarial prompts, ensuring data integrity and user privacy.

Performance and scalability are equally vital, and many NFRs in this domain share similarities with those for traditional applications, relying on precise measurements. Examples include defining strict response times, such as “The AI agent must respond to a user or another AI agent’s input within 1 second in 98% of cases.” Throughput requirements might specify that “The system should support 100 concurrent agent instances.” Scalability NFRs could mandate that “The system should scale horizontally to handle 10 times spikes in utilization with under 1% performance degradation,” ensuring consistent performance even under heavy loads.

Andrew Filev, CEO and founder of Zencoder, highlights that “Teams building AI experiences need to evaluate both what the model does and how it performs.” While functional benchmarks confirm usefulness and accuracy, they do not measure the speed or fluidity of the experience. For these aspects, classic non-functional latency metrics are crucial, including “time to first token,” “time to last token,” and “overall end-to-end agent execution latency” when using agentic AI. These metrics provide a holistic view of the agent’s responsiveness and efficiency, directly impacting user satisfaction.

Prioritizing Maintainability and Observability for AI Lifecycles

The lifecycle of an AI agent extends well beyond its initial deployment, demanding comprehensive NFRs for maintainability and observability to ensure long-term operational success. AI agent development often bundles the complexities of applications, infrastructure, automations, and AI models, making feedback loops critical for diagnosing issues and implementing continuous improvements. As organizations increasingly move towards autonomous agentic AI and agent-to-agent workflows, standardizing a list of NFRs applicable across all AI agents becomes essential.

Establishing clear observability standards is fundamental, ensuring that “all AI agents log consistent information in a centralized location.” This consistent logging is vital for efficient monitoring, troubleshooting, and performance analysis across a diverse agent ecosystem. Furthermore, the implementation of canary releases is a powerful NFR for managing updates; it allows “new AI model versions to be tested with a segmented user base and have their results benchmarked with the last stable release.” This phased rollout minimizes risks and enables careful evaluation of new models before full deployment.

ModelOps, a critical aspect of AI lifecycle management, should include NFRs that stipulate “model drift is automatically detected and used to alert development teams when retraining may be necessary.” Model drift, where the performance of an AI model degrades over time due to changes in real-world data, can severely impact an agent’s effectiveness. Automated detection and alerts ensure timely intervention, maintaining the model’s accuracy and relevance. By establishing these maintainability and observability NFRs, organizations can create robust feedback loops that are crucial for diagnosing issues, implementing operational improvements, and ensuring the sustained performance of their AI agents.

The widespread excitement surrounding the development and deployment of AI agents for productivity gains, enhanced mobile capabilities, and superior customer experiences underscores their transformative potential. However, realizing the full business value of an AI agent hinges on meticulously defining its operational and other non-functional requirements. As more businesses adopt AI agents, the necessity for developing comprehensive agentic AI architecture rules and establishing robust governance frameworks for agentic ecosystems will only grow. Organizations committed to developing and deploying AI agents must prioritize creating industry standards and learning from past experiences where applications were built without consistent operational considerations. This proactive approach ensures the development of reliable, secure, and high-performing AI agents that deliver sustainable business value.