Guardrails are checks applied before input reaches the model and after output leaves it, blocking unsafe, invalid, or out-of-scope behavior. They are implemented as rules and validators around the model rather than as prompt instructions, because prompts alone can be overridden.

Testing an agent means evaluating behavior that has no single correct answer, so the course covers writing test cases for expected properties, building evaluation criteria, and checking tool-calling sequences rather than only final text. Decision trace logging makes failures diagnosable after the fact.

No. Containers are introduced within the course, including how to package an agent and its dependencies so it runs consistently elsewhere. Basic command line comfort is enough to follow along.

Cost and latency are driven by how many model calls an agent makes, how much context each call carries, and which model handles each step. The course covers measuring both, trimming context, caching, routing simpler steps to cheaper models, and applying rate limits.

For agents it means being explicit about bias in model outputs, disclosing to users that they are interacting with an AI system, and keeping decisions auditable through logging. The course covers a practical checklist rather than a policy discussion.

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.