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Guide to Building AI Agents Effectively

May 8, 2025

Building AI Agents: A Comprehensive Guide

Introduction

  • The guide aims to distill hundreds of hours of experience in building AI agents.
  • Covers frameworks, tools, and examples for building AI agents.
  • Suitable for both non-coders using no-code tools and software engineers.
  • Sponsored by HubSpot.

Video Structure

  1. Crucial Components of AI Agents
    • Introduction to components and tools.
    • How to choose tools for each category.
  2. Agentic Workflows
    • Common workflows used today.
    • Crash course in prompt engineering.
  3. Implementation Examples
    • Examples with no-code and full code.
  4. Purpose of AI Agents
    • How to figure out the types of AI agents to build.

What is an AI Agent?

  • System that perceives, processes, and takes actions autonomously.
  • Often fulfills human roles or tasks.
  • Examples: Coding AI agents, customer service chatbots.

Implementation of AI Agents

  • Multi-agent systems often comprise sub-agents with specific roles.
  • Classic example: Customer service agents using routing workflows.

Framework for AI Agent Components

  • Components:
    1. Models
    2. Tools
    3. Knowledge and Memory
    4. Audio and Speech
    5. Guardrails
    6. Orchestration
  • OpenAI's framework includes composable primitives.

Models

  • Core intelligence of AI models.
  • Examples: GPT 4.5, 03 Mini, Claude 3.7 Sonnet.
  • Model choice depends on agent type and requirements (cost, speed, etc.).

Tools

  • Enhance models with capabilities (e.g., web search, Gmail access).
  • No-code tools like N8N facilitate tool integration.

Knowledge and Memory

  • Static Memory: Information like policies and documents.
  • Persistent Memory: Tracks interactions past single sessions.

Audio and Speech

  • Important for natural language interaction.
  • Examples: 11 Labs for voice, Whisper for transcription.

Guardrails

  • Prevent undesired behavior.
  • Examples: Guardrails AI, LangChain Guardrails.

Orchestration

  • Manages sub-agent interactions, deployment, monitoring, and improvement.
  • Tools: LangChain, Llama Index.

Agentic Workflows

  1. Prompt Chaining
    • Sequential tasks; ideal for decomposable tasks.
  2. Routing
    • Directs tasks based on categories (e.g., customer service queries).
  3. Parallelization
    • Sub-agents work simultaneously; tasks aggregated later.
  4. Orchestrator Workers
    • Complex problems with dynamic subtasks.
  5. Evaluator Optimizer
    • Iterative refinement with a circular feedback loop.
  6. Truly Autonomous Agents
    • Most autonomous; ideal for open-ended problems.

Prompt Engineering Crash Course

  • Six components: Role, Task, Input, Output, Constraint, Capabilities/Reminders.
  • Important for holding agent functionality together.

No-Code and Low-Code Examples

  • Customer Support AI Agent: Uses routing pattern via N8N.
  • AI News Aggregator Agent: Uses parallelization workflow.
  • Daily Expenses Tracker: Aggregates inputs, provides daily summaries.

Coded Example

  • Financial Research Assistant: Uses OpenAI's agents SDK.
  • Employs routing design workflow.

Choosing AI Agents to Build

  • Start with personal needs or shadow others for problem identification.
  • Think of AI agents as equivalents to SaaS companies.

Tech Innovations and Trends

  • Advances in voice, audio, image, and video models.
  • Focus on fundamental components and frameworks.

Conclusion

  • Stay updated with major innovations.
  • Build skills and projects to align with industry demands.
  • Be patient and persistent in building AI capabilities.

Full transcript