Develop multi-agent solutions in Azure
Development questions focus on how agents are actually wired in Azure AI Foundry and the Microsoft agent ecosystem. You must know the moving parts: agent definitions (model deployment, instructions, tools), tools (function calling, Azure AI Search, code interpreter, OpenAPI, Azure Functions, MCP-style connectors), threads/conversations for state, and orchestration code that routes between agents. Understand the distinction between a hosted agent service that manages threads and runs for you versus bringing your own orchestration framework (for example, Semantic Kernel or the Microsoft Agent Framework) where you own the control loop. Know that tool invocation is model-driven: the model emits a tool call, your code executes it and returns results, and you must handle errors, timeouts, and retries.
Traps: assuming the platform automatically chains agents (it does not - you write the routing), confusing a model deployment with an agent, and forgetting that tool schemas and descriptions drive selection accuracy. Another frequent trap is statelessness - if you do not persist thread or conversation state, agents lose context between turns. Also expect questions on idempotency for tool calls that mutate systems, and on grounding: retrieval tools must be connected and cited rather than relying on model memory.
Rule of thumb: treat every tool call as an unreliable network boundary - validate inputs, set timeouts, make mutating calls idempotent, and return structured errors the model can reason about.
Worked example: an orchestrator agent receives a request, calls a router function to select a specialist, passes a structured JSON payload (request id, extracted fields, constraints), and the specialist calls Azure AI Search for grounding. Persist the thread id so a follow-up turn resumes context; wrap the search call in retry with a timeout and return a typed error object on failure so the orchestrator can fall back gracefully.