Landscape Guide: Building Applications with AI Agents
Explore the practical landscape of building AI agent applications by understanding key development frameworks like LangChain, AutoGen, and CrewAI. Learn to select the right tools for multi-agent or single-agent systems, and discover prominent production use cases such as intelligent coding assistants, customer experience automation, and autonomous data pipelines. Understand engineering challenges including hallucination, cost management, observability, and safety to design robust and adaptive AI agent systems.
By the end of this lesson, you will be able to:
Explain why specialized frameworks are used to build AI agent applications.
Compare the major agent development frameworks and understand the trade-offs between them.
Identify prominent real-world AI agent applications across key industries.
Understand the practical engineering challenges that arise when moving from agent design to production deployment.
The previous lessons in this ...