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DataCampBuilding Scalable Agentic Systems
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Korshub's take
Mastering Agentic Scalability
Moving AI agents from a simple prototype to a production-ready system requires more than just good prompting. This course focuses on the architectural foundations necessary for growth, specifically focusing on the three pillars: modularity, robustness, and adaptability.
Key Learning Areas
- Interoperability: Understand how to leverage the Model Context Protocol (MCP) from Anthropic and Google's Agent-to-Agent (A2A) protocol to create seamless data integrations.
- Risk Mitigation: Learn to implement comprehensive testing frameworks that address component performance, security, and integration issues.
- Deployment Strategy: Determine the most effective deployment types based on your specific use case requirements.
This is an essential resource for developers looking to bridge the gap between experimental AI scripts and reliable, scalable agentic systems.
About this course
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 PracticesBefore 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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