Building an AI agent is becoming easier. Knowing whether you can trust it in production is not.

An agent can successfully call a tool, retrieve information and produce a convincing answer while still failing in ways that are difficult to notice. It can choose the wrong tool, misunderstand context, invent an answer when retrieval fails, or take an action that was technically valid but operationally wrong.

This session takes a practical approach to testing AI agents on Azure. We will deliberately break an agent and examine what happens when tools fail, retrieved context is incomplete, instructions conflict, responses are incorrect, or an external dependency becomes unavailable.

We will explore practical patterns for evaluating agent behaviour, validating tool calls, handling failures, adding guardrails and observing agent workflows before they become production incidents.

The goal is not another session showing how to build an impressive AI agent. It is to answer the harder question: how do you know your agent is ready to be trusted?