Building Scalable Agentic Systems
Why do so many agentic systems fail to traverse the gap between development and production? In this course, you'll find out! Learn how to design and develop AI agents with scalability in mind, following the three pillars of agentic scalability: robust infrastructure and tooling, modular design architecture, and continuous evaluation and feedback loops. You'll learn about how agentic frameworks like the Model Context Protocol (MCP) and the Agent-to-Agent protocol (A2A) enable cleaner and faster integrations between data sources and agents. Finally, learn about the tests and deployment strategies to set your agent up to deliver value.
Practical information:
Georganiseerd door:
Building Scalable Agentic Systems
Designing Scalable Agents
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.
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.
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.
What you'll learn
- Assess testing, monitoring, and rollout strategies that support reliable, cost-effective deployment of multi-agent applications
- Differentiate network and supervisor multi-agent design patterns with respect to task delegation and communication flow
- Evaluate the suitability of interoperability frameworks such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) for given integration scenarios
- Identify common architectural components and design principles that enable scalability in AI agentic systems
- Recognize typical failure modes and risk factors that arise when deploying agents at production scale
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