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AI Agents &

- Workflows - The Practical Guide

Getting Started

- Welcome To The Course! (1:22)

- What Is An AI Agent? (2:13)

- General vs Task-specific Agents (2:39)

- Where Agents Run / Execute (2:07)

- AI Agent Harnesses, LLMs &
- Limitations (3:25)

- How Agents Use Tools (5:15)

- Understanding Session Context (2:17)

- Core AI Agent Building Blocks - Overview (2:25)

- AI Agents vs AI Workflows (2:18)

Using &

- Steering AI Agents

- Module Introduction (0:57)

- Three Main Ways For Controlling AI Agents You Should Know (0:49)

- Managing Agent Tools &
- The Environment (3:42)

- Choosing The Right Model (1:17)

- Understanding Developer-provided System Instructions (1:39)

- Providing Your Own Instructions &

- Understanding AGENTS.md / CLAUDE.md (3:29)

- Understanding Agent Skills (8:28)

- Understanding Agent Memory (3:23)

- Sometimes Important: Humans In The Loop (2:57)

Building Agents &

- Workflows - An Overview

- Module Introduction (0:36)

- Options For Building Agents &
- Workflows (2:46)

- WHO Builds It, WHERE Does It Run? (3:36)

- Options When Building WITH Code (0:52)

- Choosing Your Agent Building Blocks (1:56)

Building AI Workflows &

- Applications

- Module Introduction &
- Expectations (1:35)

- Building Workflows - The Basics (1:20)

- Workflows vs Agents (2:39)

- Building Visually with n8n - First Steps (2:38)

- Running a Demo n8n Project Locally (1:41)

- Exploring n8n &

- Its Workflow Builder (6:44)

- Exploring &

- Understanding a Realistic Workflow (4:24)

- Onwards To A Code-based Solution! (3:15)

- Exploring &

- Understanding A Code-based Workflow (6:20)

- Module Summary (1:11)

Building AI Workflows &

- Applications [LEGACY]

- About This LEGACY Section

- Module Introduction (2:21)

- No Code vs With Code (2:07)

- Building AI Apps &
- Using AI Programmatically (4:12)

- Proprietary vs Open (Local) LLMs (5:51)

- Using Open LLMs

- Understanding Our Development Environment (2:29)

- Creating a New Python Project (using "uv") (1:43)





- OpenAI Setup &

- Pricing (5:41)

- Getting Started With A First Example Workflow (2:44)

- Preparing HTTP Requests For The OpenAI API (8:40)

- Choosing &

- Using a Model (2:04)

- Prompt Engineering (4:35)

- Extracting &

- Using the LLM Response (4:50)

- More on the OpenAI API &

- SDK

- Code Deep Dives vs Provided Code Snippets

- Use Those Docs! (1:38)

- Using The OpenAI Python SDK (5:49)

- Leveraging Few-Shot Prompting (4:02)

- Generating Prompts Dynamically With Dynamic Content (2:12)

- Building Multi-Step &

- Multi-Model Workflows (6:47)

- Workflows vs Agentic Systems (1:34)

- Using Locally Running Open Models via Ollama (8:08)

- Enforcing &

- Using Structured Outputs (10:53)

- More On JSON Schemas &

- Structured Outputs

- Structured Outputs via SDK &

- Pydantic (3:56)

- Using Prompt Engineering To Control Output

- Onwards To Another Example (5:38)

- Generating Images In a Workflow (6:27)

- Controlling Workflow Execution with Control Flow Adjustments (2:45)

- Control Flow In Action (8:42)

- Adding a "Human In The Loop" (6:51)

- Integrating External Services - Example: Slack (6:21)

- Important: Potential Problems &

- Security Risks

Build AI Agents

- Module Introduction &
- Expectations (1:55)

- Setting Up &

- Starting the n8n Demo Project Server (3:08)

- Creating Agents Visually With n8n (3:42)

- Understanding Tools &

- The Agent Harness (in n8n) (2:51)

- Running The n8n Agent (2:45)

- Finding Key Agent Building Blocks in n8n (0:59)

- Onwards To Code-based Agents! (3:53)

- Defining Tools As Functions (2:26)

- Analyzing The Agent Loop (3:23)

- How The Agent Learns About Tools &

- Behaves Correctly (6:03)





- Using Provider-native Tooling To Make Things Easier (5:14)

- Onwards To A General Agent (1:43)

- Analyzing Tools, Instructions &

- Context Engineering For A General Agent (6:19)

- Demo: Using The General Agent (3:15)

- Analyzing An Example Sandbox Implementation (2:44)

- Adding A Human To The Agent Loop (2:35)

- How Agent Execution Is Constrained (3:21)

Building AI Agents [LEGACY]

- About This LEGACY Section

- Module Introduction (1:36)

- How LLMs (Do Not) Use Tools (6:04)

- Implementing Tool Use From Scratch (11:24)

- Using OpenAI's Function Calling Feature (10:23)

- Building a Multi-Tool Versatile Agent (11:16)

- Using Advanced AI Models

- Building Reusable Elements With Classes (7:53)

- Getting Started with a Multi-Agent System (7:14)

- Extracting Website Content

- Building &
- Connecting Specialized Agents (10:24)

- Universal vs Specialized Agents (3:38)

- Agent Memory: Short-Term &

- Long-Term (5:21)

- Wrap Up (1:09)

Build Agents With Frameworks - With "eve"

- Module Introduction (0:52)

- Library &
- Framework Options - An Overview (0:58)

- Libraries vs Frameworks (2:33)

- An Introduction To The "eve" Framework (2:23)

- Diving Into An "eve" Project (2:53)

- Configuring Tools For The "eve" Agent (2:32)

- Connecting The Agent To A Knowledge Base &

Skills (3:52)

- Seeing The "eve" Agent In Action (3:58)

- Deep Dive: Connecting Slack As A Channel (9:06)

- More On Tunnels / cloudflared

- Onwards To CrewAI (0:34)

Using CrewAI: A Third-Party AI Agents SDK

- Module Introduction (2:36)

- Getting Started With CrewAI (4:56)

- Understanding CrewAI Agents (5:53)

- Using CrewAI Tasks (3:25)

- Adding Tools To Agents (4:14)

- Running The Crew (3:11)

Roundup

- Course Roundup (1:10)

AI Agent Harnesses, LLMs &
- Limitations

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📌 AI Agent Harnesses, LLMs & Limitations (Cundinamarca)
🏢 Academind
📍 Cundinamarca