This lesson develops practical capability in Agentic AI, Tools, Memory, Multi-Agent Orchestration, and Application Integration as part of Advanced Azure AI Foundry. 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 agentic ai, tools, memory, multi-agent orchestration, and application integration. 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, deploying, and optimizing advanced generative AI solutions in Azure AI Foundry with prompt engineering, retrieval augmented generation, agents, multi-agent orchestration, model evaluation, safety, monitoring, performance, and governance. Produce a concise plan, implementation or analysis evidence, a validation checklist, a troubleshooting record, and a prioritized recommendation for the next iteration.