This lesson develops practical capability in Evaluation, Security, Deployment, Monitoring, Cost, and Operational Improvement as part of Azure AI Foundry + Agents + GitHub. Learners connect the concepts to a realistic workplace objective, perform guided work, validate evidence, and document improvements.
The lesson begins by defining the architecture, stakeholders, information flow, operating assumptions, and quality expectations that shape evaluation, security, deployment, monitoring, cost, and operational improvement. It distinguishes essential decisions from optional enhancements and explains how security, governance, usability, cost, and supportability influence the final design.
Learners then map the end-to-end workflow, identify control points, and choose appropriate configuration or analysis steps. Special attention is given to dependencies, permissions, data boundaries, exception handling, documentation, and the evidence required for an accountable operational handoff.
Apply the lesson to a scenario in which an organization must improve building generative AI applications and agents with Azure AI Foundry and GitHub through model selection, prompt engineering, retrieval, tools, multi-agent workflows, GitHub Copilot, Agentic DevOps, evaluation, deployment, security, and operations. Produce a concise plan, implementation or analysis evidence, a validation checklist, a troubleshooting record, and a prioritized recommendation for the next iteration.