What’s Next?
Explore the challenges and strategies for designing AI systems using agentic patterns. Learn to balance simple and complex solutions, understand real-world pattern integration, and apply evaluation methods for effective AI agent creation. This lesson guides you to build practical, adaptable AI solutions with a focus on ongoing improvement and real-world application.
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You’ve learned how to design AI using agentic patterns, like simple chains or complex orchestrators. Think of these as your AI blueprints. But in the real world, these blueprints often blend:
Patterns aren’t always separate: In practice, AI systems are complex, like a mosaic. It’s tough to draw clear lines between patterns.
For instance, when an AI decides to run tests or analyze code, is it just routing a task, or is the orchestrator choosing the right tool?
When a research AI discards a bad source, is it a simple ReAct loop (observe, act, reflect), or a mini evaluator-optimizer cycle? Often, it’s both!
One part of your AI can use ideas from many patterns at once. This isn’t a problem; it shows your system is mature. The patterns simply give us a common language to handle this complexity.
Build the right system, not just the smartest: ...