From the course: Hands-On AI: Building Agents with the Google Agent Development Toolkit (ADK)

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Custom agents: Ultimate flexibility with Python logic

Custom agents: Ultimate flexibility with Python logic

From the course: Hands-On AI: Building Agents with the Google Agent Development Toolkit (ADK)

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Custom agents: Ultimate flexibility with Python logic

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- [Instructor] What if you're building Wanderwise for business travelers? You want your AI to not only suggest itineraries and packing lists, but also enforce company travel policies like flagging trips that exceed budget, require special approval, or must-avoid certain destinations. Can a language model or a standard workflow agent handle all of that? Not quite. Let's see how custom agents let you implement logic and integrations that LLM and workflow agents simply cannot handle. We'll discover how they're essential for building truly production-ready enterprise AI systems. LLM agents are great at reasoning and language tasks, but they're limited to what the model can infer and the tools you've connected. Workflow agents like sequential, parallel or loop are perfect for running agents in a set order or pattern, but they follow fixed orchestration logic. Neither is designed for highly conditional business rules, dynamic agent selection based on complex criteria or deep custom…

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