Between first silicon and production ramp, test engineers are often diverted from valuable and fulfilling work like device debug by time-consuming and tedious processes such as communications bring up, characterization, correlation, and test time reduction. Increasing team size can help compress time to market, but this approach faces diminishing returns as coordination overhead increases and other factors like tester and device availability become bottlenecks. Artificial Intelligence (AI) Agents provide a new means to boost the productivity of each individual engineer, improving time to market with existing teams. But this promise can only be achieved if AI agents are reliable, context-aware, and trustworthy.
Measuring reliability in a credible way requires a repeatable test framework. Humans can define prototype test cases, but to gain confidence in an open-ended system, many variations must be tested, for which manual creation is not practical. Since agentic AI system naturally have some variability, tests must be repeated multiple times to gain confidence in their reliability.
Context awareness and intelligence are closely related characteristics that help make an AI Agent helpful and pleasant to work with. Task-based intelligence requires prompting AI models with expert knowledge and up-to-date contextual information, such as which step of a process the AI and user are working on. Explicitly communicating the same context to the user can help solve misunderstandings between humans and AI. Agent intelligence and context awareness must be checked within the reliability testing framework.
Users may under-trust a tool, which causes organizations to miss out on the productivity gains the tool could provide. Over-trust is more dangerous, since it can result in quality escapes or even damaged parts or equipment. Explicitly communicating trust levels and making the AI dynamically adjustable is the key to keeping users productive and engaged. An agent that asks for a lot of confirmation by default is helpful at first but annoying in the long run. For this reason, a helpful-feeling agent must allow the user to adjust the level of confirmation in a fine-grained and reversible way. Over-trust can be mitigated by requiring users to add absolute limits to their test programs, providing guardrails on what the AI can do on its own.
This poster will discuss: 1) a repeatable testing framework that delivers agent reliability; 2) dynamic prompting that gives models context awareness and intelligence; 3) explicit trust building that improves the usefulness of AI agents.
Reliability + context awareness + trust = AI agents that boost engineer productivity and are pleasant to use.
