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Anna Liashenko has completed

Building Scalable Agentic Systems

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1 hr 30 min
1,750 XP
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Course Description

Design and Develop Agents for Scaling

Learn how to design and develop AI agents with scalability in mind, following the three pillars of agentic scalability: modularity, robustness, and adaptability. Discover what makes a successful agent in production, and why so many struggle to get there.

Discover the Power of MCP and A2A

The Model Context Protocol (MCP) developed by Anthropic has revolutionized agent interoperability, creating a unified approach for connecting agents to data sources. The Agent-to-Agent protocol (A2A) developed by Google compliments MCP. Find out how these two frameworks can be combined to ensure your agent's integrations are scalable.

Implement Agent Testing and Deployment Best Practices

Before pressing the big red button and launching your agent into production, you've got to mitigate the risks that come with scaling. Learn how to create a robust testing framework to capture issues with components, integrations, performance, and security. Decide which deployment type is right for your agent by looking at the needs of the use case.
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  1. 1

    Designing Scalable Agents

    Free

    Discover what makes a successful AI agent in production (and how many of them fail on the way!) Learn about the key agentic design principles to set up your agents for scaling, including robust infrastructure and tooling, modular design architecture, and continuous evaluation and feedback loops.

    Play Chapter Now
    AI Agents in the Wild
    50 xp
    To agent or not to agent
    100 xp
    Agentic applications
    50 xp
    Design Principles for Scalable Agents
    50 xp
    Strategies for scalable design
    100 xp
    Enforcing the pillars of scalable design
    50 xp
    How AI Agents Scale (and Fail)
    50 xp
    Don't fail when you scale!
    50 xp
    Agentic accounting
    100 xp
    Guardrails or it fails
    50 xp
  2. 2

    Developing Agents for Scalability

    Learn about key strategies to ensure that your agent is being developed with scalability in mind. Gain insights into how the Model Context Protocol (MCP) and the Agent-to-Agent protocol (A2A) enable scalability through standardization.

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    Multi-Agent Design Patterns
    50 xp
    Making the leap from single to multi-agents
    50 xp
    Multi-agent design patterns
    100 xp
    The Model Context Protocol (MCP)
    50 xp
    Getting to know MCP
    50 xp
    MCP is the MVP
    50 xp
    Clients, servers, and everything in between
    50 xp
    The Agent-to-Agent (A2A) Protocol
    50 xp
    Agent cards - Gotta Catch 'Em All
    50 xp
    MCP and A2A: friend or foe?
    100 xp
  3. 3

    Deploying Agents into Production at Scale

    Time for production, but not so fast! Build a robust testing framework to give you confidence that the AI agent will continue to perform in production. Choose the best deployment strategy for your use case, and learn how to integrate real-time data sources with your agentic system.

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    From Proof-of-Concept to Production
    50 xp
    Robustly testing an agent
    50 xp
    What's left to check?
    100 xp
    Fast and Efficient Data Ingestion
    50 xp
    The value of real-time data
    50 xp
    Failing Gracefully: Mitigating Risks
    50 xp
    Retry with exponential backoff
    50 xp
    Laying down the law!
    50 xp
    Congratulations!
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

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collaborators

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James Chapman

prerequisites

Introduction to AI Agents
Korey Stegared-Pace HeadshotKorey Stegared-Pace

Senior AI Cloud Advocate, Microsoft

Korey Stegared-Pace is an Senior AI Cloud Advocate at Microsoft, focused on community education about the capabilities of Generative AI. It has been his personal mission to expand AI literacy by speaking at meetups, conferences, and hosting technical workshops. As the content lead for Microsoft’s 'Generative AI for Beginners' course, he has reached a global audience of over 1 million views. When not thinking about AI, he enjoys chasing after his two young children, along with his wife in Stockholm, Sweden.
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