AI Studio - Creating and scaling your custom copilots
summary
Azure AI Studio shows how copilots move beyond simple RAG into multi-agent systems that combine LLMs, metaprompts, functions and APIs, and it demonstrates three workflow patterns: fixed, partially dynamic and fully dynamic. It uses the Azure OpenAI Assistant API and frameworks like autogen langchain and semantic kernel for orchestration, offers tracing via the Promflow SDK and emits OpenTelemetry spans for per-call debugging, provides local chat UI, evaluators for safety and groundedness, CI/CD integration, and content safety checks for jailbreaks and IP.
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chapters (11)
- 0:00Introduction to agent systems
- 0:58Defining agents and multi-agents
- 1:47Orchestrating agent workflows
- 3:44Fixed workflow examples
- 4:35Dynamic workflow examples
- 6:02Fully dynamic systems
- 6:56Troubleshooting agent errors
- 8:45Tracing, debugging, and testing
- 12:05Production deployment and CI/CD
- 12:46Monitoring and safety
- 13:43Future trends in AI
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