Reliable AI Agents: Evals, Guardrails & MCP

About this course

This course contains the use of artificial intelligence.

A convincing answer is only the beginning. Can your AI workflow reject an unsafe action, retry for the right reason, respect an approval, and show what actually happened?

Reliable AI Agents: Evals, Guardrails & MCP gives you a practical way to answer those questions in Python. Follow one support-ticket workflow from a proposed action to a controlled local result, and learn how to turn reliability requirements into explicit checks you can inspect, run, and challenge.

Across ten focused lessons, you will define what success means, validate structured outputs, separate recoverable errors from policy denials, and put clear limits around retries and tool permissions. You will examine why an approval must match a particular payload and expiry, how a SQLite transaction can prevent repeated delivery from creating a second identical effect, and how a small MCP client and server exchange messages between local processes.

The goal is useful engineering judgment. Instead of relying on a final response that looks correct, you will inspect attempted actions, event order, and resulting state. You will practice asking which rule should stop a failure, what evidence demonstrates that the rule worked, and which assumptions still need testing.

You will work with a downloadable Python project, a learner guide, a complete course reference, guided workbook exercises, three short coding tasks, quizzes, and answer keys. Quick references, an agent-reliability vocabulary sheet, and a framework comparison help you revisit the ideas and relate them to other systems. The included companion notebook provides another way to work through the supplied project.

Your final assignment brings the pieces together: document a concise architecture card, implement one bounded local extension, and write a negative test that challenges it.

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