Azure AI Agent Service

Start with a sandboxed agent workflow for managed platform for building, deploying, and scaling enterprise AI agents with models, tools, identity, and observability. Define the model, tools, data sources, identities, permissions, expected output, timeout behavior, and actions that require human approval.

Use Azure AI Agent Service on representative tasks rather than a polished demo case. Inspect sources, intermediate reasoning artifacts where available, tool calls, generated content, and any action the system proposes. Correct bad assumptions early and preserve examples that reveal recurring failure modes.

Introduce automation gradually. Keep sensitive data and high-impact actions behind appropriate permissions and approval gates. For learning tools, require students to verify explanations and follow academic-integrity rules; for workplace agents, log actions and make destructive changes reversible.

After rollout, monitor accuracy, completion rate, overrides, latency, cost, user feedback, and integration failures. Revalidate the workflow whenever the provider changes models, connected systems, agent permissions, or product behavior.