Ricardo
Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure
#1about 3 minutes
Understanding the core components of an AI agent
AI agents evolve beyond simple chatbots by using large language models, instructions, and tools to integrate directly into business processes.
#2about 2 minutes
Solving developer challenges with Azure AI Foundry
Azure AI Foundry provides a comprehensive platform to address common developer challenges like model selection, security, and observability when building AI agents.
#3about 1 minute
Exploring the Azure AI Foundry Agent Service
The Agent Service offers enterprise-grade features including orchestration, SDK integrations, knowledge tools, and built-in content safety for robust agent development.
#4about 2 minutes
The development lifecycle from ideation to production
The AI application lifecycle consists of ideation, implementation, and operations, starting with low-cost experimentation in GitHub Models before moving to Azure Foundry.
#5about 4 minutes
Experimenting with prompts and models in GitHub
Use GitHub Models to rapidly prototype by comparing different prompts and models against test datasets and using evaluators to generate performance KPIs.
#6about 3 minutes
Integrating evaluation and monitoring into your workflow
Implement a robust evaluation strategy by incorporating KPI checks into CI/CD pipelines and using continuous monitoring with end-to-end tracing in production.
#7about 7 minutes
Building a multi-agent contract analysis application
A practical example demonstrates a multi-agent system that analyzes contracts, checks compliance, and uses automated evaluations for continuous quality assurance.
#8about 2 minutes
Choosing the right multi-agent interaction pattern
Design effective multi-agent systems by selecting the appropriate interaction pattern, such as sequential, concurrent, or group chat, based on your process and outcome goals.
#9about 2 minutes
Implementing agent workflows with Semantic Kernel
Use the Semantic Kernel agent and process frameworks to implement complex multi-agent workflows and deploy them scalably across various environments.
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Matching moments
02:40 MIN
Key takeaways for implementing AI agent solutions
Infrastructure as Prompts: Creating Azure Infrastructure with AI Agents
01:27 MIN
Getting started with the Azure AI Foundry platform
How Mixed Reality, Azure AI and Drones turned me into a Magician?
03:00 MIN
Exploring the capabilities of Azure AI Foundry
Azure AI Foundry for Developers: Open Tools, Scalable Agents, Real Impact
03:57 MIN
Designing a multi-agent system for Azure infrastructure
Infrastructure as Prompts: Creating Azure Infrastructure with AI Agents
07:19 MIN
Demo of an agentic system for Azure architecture design
Rethinking Workflows in the Agentic Era
04:27 MIN
How the Azure AI platform supports the GenAIOps journey
From Traction to Production: Maturing your GenAIOps step by step
04:59 MIN
Introducing the Azure AI platform for end-to-end LLMOps
From Traction to Production: Maturing your LLMOps step by step
02:48 MIN
Understanding the core components of agentic AI systems
Agentic AI Systems for Critical Workloads
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