Intro to AI Agents and Agentic AI

Explore how AI agents adapt to dynamic environments by integrating digital data and real-world factors to make intelligent recommendations, from self-driving cars to shopping assistants.

Explore how AI agents interpret multimodal sensor data with a model that acts as a brain, turning perceptions into informed decisions and real-world actions.

The lecture demonstrates how a decision making logic layer uses structured rules to evaluate model outputs and choose actions, with trading and spam filtering examples.

Agents observe the environment after acting, process fresh input data, and refine their behavior through a continuous feedback loop to improve future performance.

Explore AI agent structures by complexity, starting with simple reflex agents and advancing to advanced designs to help you choose and build the right agent for your objectives.

Distinguish llms ai workflows from ai agents, explore the React and Riwu frameworks, compare single-agent and multi-agent systems, and examine chain-of-thought reasoning that enhances decision making and interpretability.

AI agents reason about the environment and then act to schedule meetings, pick a meeting location, check weather, reserve a cafe, and send invitations, all within the ReAct framework.

Evaluate AI models by balancing accuracy, speed, cost, and scalability; prototype with the most capable model, establish a baseline, then experiment with alternatives to optimize efficiency.

Explore how external tools and APIs let agents access data and perform actions within software, using data tools for retrieval and action tools for tasks like scheduling and tickets.

Learn prompt engineering best practices to configure agent system prompts, break down tasks, define clear actions, and anticipate edge cases for reliable, high quality AI agent workflows.

Master zero-shot, one-shot, and few-shot prompting to configure AI agent systems, and compare these techniques with fine-tuning to understand how guidance shapes performance.

Adopt layered guardrails for AI agents, combining data privacy, content safety, misalignment prevention, brand alignment, and accuracy to reduce risk and prevent hallucinations.

Integrate human intervention early in artificial intelligence deployment to catch failures with human oversight. Implement escalation mechanisms that hand control to humans when uncertain.

Explore building ai workflows and agents with n8n, a drag-and-drop automation tool that needs little to no coding, and start from scratch or test a simple ai agent.

Define the agent's role with a system prompt to act as a personal assistant drafting work emails, using user context and providing a structured greeting, body, closing, and signature.

Add memory to your n8n agent using a simple memory with a five-interaction context window, then verify it remembers user-provided details like the name.

Configure an output node with manual mapping and a json field to surface agent responses into chat, using Google Sheets and Gmail, test by emailing Ned at 10:00 Monday.

Explore frameworks for building AI agents, including Lang chain with chains and agents, Lang Chain Graph, memory and tool calling, and orchestration, Microsoft Autogen, Cru AI, Google ADK, Flow Wise.

Deploy your AI agent from a notebook to a live environment via a chat interface, using AWS Lambda, bedrock SageMaker, and Lex for scalable, reliable web communication.

AI agents offer cost efficiencies by automating tasks and enabling 24/7 customer service, while delivering real-time insights to decision makers and freeing humans for meaningful, strategic work that drives growth.