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Building Scalable Agentic Systems

BasicSkill Level
4.8+
1,768 reviews
Updated 12/2025
Discover what it takes to scale AI agents, with a little help from frameworks like MCP and A2A.
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TheoryArtificial Intelligence1 hr 30 min10 videos29 Exercises1,750 XP12,270Statement of Accomplishment

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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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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

Prerequisites

Introduction to AI Agents
1

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.
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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.
Start Chapter
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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Building Scalable Agentic Systems
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*4.8
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  • Khadidja Nour el houda
    4 hours ago

  • Ming
    14 hours ago

  • Κωνσταντίνος
    14 hours ago

  • Angelo Emmanuel Alvarez
    18 hours ago

  • Jayanthi
    yesterday

  • Russell
    yesterday

Khadidja Nour el houda

Ming

Κωνσταντίνος

FAQs

Is this course suitable for beginners?

This course assumes some familiarity with AI agents concepts. It is best suited for developers and engineers who want to take AI agents to production.

What are the three pillars of agentic scalability covered in this course?

The course is structured around robust infrastructure and tooling, modular design architecture, and continuous evaluation and feedback loops, applying each across design, development, and deployment.

What protocols does this course cover and why do they matter?

The course covers the Model Context Protocol (MCP) and the Agent-to-Agent protocol (A2A), both of which standardize how agents connect to data sources and communicate with each other, making integrations faster and cleaner at scale.

What does the course teach about deploying agents to production?

The third chapter covers testing frameworks, real-time data ingestion, deployment strategies, and risk mitigation techniques such as retry with exponential backoff and guardrails for graceful failure.

Why do so many agentic systems fail in production and does this course address that?

Yes, that question is central to the course. The first chapter examines common failure modes when scaling agents and teaches design principles specifically aimed at preventing them.

Who will benefit most from this course?

Engineers and developers who are moving agentic projects from proof-of-concept to production and need a structured approach to scalability, testing, and deployment.

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