Key insights

• 🔍 Clear distinction between workflows and agents
• 🏗️ Start with the simplest solution when building applications with LLMS
• 🧩 The choice of underlying patterns is crucial for building effective AI systems
• 🔑 Key building blocks for AI systems include augmented LLMS, retrieval, tools, and memory
• 📝 Tracking updates with a tracking number and memory in the context of past interactions with language models (LLMS)
• 🔄 Using prompt chaining, routing, and parallelization for creating smarter applications with AI
• ⚙️ Implementing orchestrator-worker flows, parallel processing, and evaluator-optimizer cycles to speed up applications
• 🎯 Prioritize deterministic workflows over complex agent patterns and start simple for more reliable solutions
• 📊 Understand agentic patterns, scaling challenges, proper testing, guard rails, and optional learning resources for building and scaling AI applications

Q&A

• What are the tips for building and scaling AI applications?

  The tips include understanding agentic patterns, not underestimating scaling, implementing proper testing, using guard rails, and considering optional learning resources for additional support.

• What are some challenges in developing AI systems?

  Developing AI systems using different patterns and workflows can be challenging, and while some patterns are straightforward to implement, achieving reliable results is difficult.

• How can developers create smarter applications with AI?

  Developers can create smarter applications by understanding concepts like tracking, memory, and context and employing effective workflow patterns such as prompt chaining, routing, and parallelization.

• How can developers speed up applications using workflow patterns?

  Workflow patterns enable parallel processing, orchestrator-worker flows, and evaluator-optimizer cycles, which can significantly speed up applications.

• What are the key building blocks for AI systems?

  The key building blocks for AI systems include augmented LLMS, retrieval, tools, and memory, essential for creating effective AI solutions.

• What is the distinction between AI systems and AI agents?

  AI systems control the flow of applications and data using patterns, while AI agents operate in a specific environment, often in a loop, to achieve a goal using environmental feedback.

• What will the video cover?

  The video will share practical tips, techniques, and insights for developers on building effective and reliable AI agents, distinguishing between AI systems and AI agents.

• 00:00 Building effective and reliable AI agents is challenging, and many online examples are just cool demos. The video will share practical tips and techniques for developers based on the speaker's experience and insights from leading companies. The distinction between AI systems and AI agents will be explained.
• 03:42 The distinction between workflows and agents is crucial for developers to understand. When building applications with LLMS, it's important to start with the simplest solution and only increase complexity when needed. Building effective AI systems depends on the underlying patterns used to control the flow of application and data. Key building blocks for AI systems include augmented LLMS, retrieval, tools, and memory.
• 08:19 Understanding the concepts of tracking, memory, and context is essential when working with language model APIs. Prompt chaining, routing, and parallelization are three effective workflow patterns for creating smarter applications with AI.
• 12:37 Workflow patterns can help speed up applications by enabling parallel processing, orchestrator-worker flows, and evaluator-optimizer cycles. Agent patterns are more agentic, operating in a loop within a specific environment to achieve a goal.
• 16:45 Developing AI systems using different patterns and workflows can be challenging. While some patterns are straightforward to implement, achieving reliable results is difficult. Prioritizing deterministic workflows over complex agent patterns and starting simple can lead to more reliable solutions.
• 20:40 Tips for building and scaling AI applications: Understand agentic patterns, don't underestimate scaling, implement proper testing, use guard rails, consider optional learning resources.