The Definitive Guide to Building AI Agents in 2025 - Frameworks, Patterns and Use Cases!
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How to Build AI Agents in 2025: A Comprehensive Guide - Frameworks, Patterns, Use Cases and Resources
In this video, I explore Anthropic's insights on designing and implementing effective agents using large language models (LLMs). Drawing from their article and combining with our industry experience, we'll break down the core principles and strategies for creating flexible, reliable, and scalable agentic systems. Whether you're a beginner or an experienced developer, this guide provides actionable advice to help you maximize the potential of AI agents in your applications.
🔗 Links & Resources:
Skool: https://www.skool.com/ai-software-dev...
Code the Revolution: Newsletter - https://aidev9.substack.com/
Discord server: / discord
Building Effective Agents (Anthropic) - https://www.anthropic.com/research/bu...
PydanticAI Masterclass: • PydanticAI Masterclass
LangGraph Tutorials: • LangGraph Tutorials
📌 Key Topics Covered:
What are agents and how they differ from workflows
The importance of simplicity and composable patterns
When to use workflows versus agentic systems
Enhancing LLMs with retrieval, tools, and memory
Using prompt chaining for complex tasks
Balancing trade-offs between cost, latency, and performance in autonomous agents
Best practices for debugging and implementing LLM-powered systems
👩💻 Why Watch?
Gain practical tips and best practices from Anthropic’s extensive experience with agent development across industries. Learn how to design systems that combine flexibility, efficiency, and reliability to tackle even the most complex tasks.
This video focuses on the development of AI agents in the year 2025. The document outlines various aspects of agent construction, including:
Agent definitions and usage
Frameworks for agent development (e.g., LangGraph, Microsoft Autogen, Amazon Bedrock, Rivet, Vellum)
Distinction between visual and code-based frameworks
Specific coding frameworks such as LangChain/LangGraph and PydanticAI
Use cases and agent patterns, including:
Prompt Chaining
Routing
Parallelization
Orchestrator-Workers
Evaluator-Optimizer
Considerations for choosing a framework, such as:
Use case criticality
Team skills
Time and budget constraints
Integration requirements
AIOps needs
Preference for visual, no-code, or low-code approaches
Resources for no-code, low-code, and enterprise tools, as well as educational and community resources.
🔗 Links & resources:
Skool: https://www.skool.com/ai-software-dev...
Newsletter: https://aidev9.substack.com
Discord server: / discord
GitHub repo: https://github.com/aidev9/tuts
PydanticAI: https://ai.pydantic.dev
Ollama: https://ollama.com
Timecodes:
00:00 - Introduction to AI Agents Landscape in 2025
00:30 - Building Effective AI Agents
00:47 - What are AI Agents?
01:30 - AI Agent Definition according to Anthropic
02:08 - When (and when not) to use agents
02:43 - When and how to use frameworks
03:13 - What is LangChain and LangGraph
04:18 - What is Microsoft Autogen
04:54 - What is Amazon Bedrock
05:36 - Rivet Introduction
06:18 - Vellum Introduction
06:52 - AI Agents Market Landscape
08:45 - Visual vs. Coding AI Agent Frameworks
09:21 - LangGraph Tutorials
09:50 - PydanticAI Tutorials
10:35 - Use Cases for AI Agents
14:36 - Agent Patterns Workflow: The augmented LLM
15:36 - Agent Patterns Workflow: Prompt chaining
16:56 - Agent Patterns Workflow: Routing
18:08 - Agent Patterns Workflow: Parallelization
18:48 - Agent Patterns Workflow: Orchestrator-workers
20:18 - Agent Patterns Workflow: Evaluator-optimizer
21:20 - Agent Patterns: Autonomous agents
22:45 - Example: High-level flow of a coding agent
24:15 - How to Choose an AI Agent Framework
26:11 - Low Code AI Agent Framework: FlowiseAI
27:02 - Coding AI Agent Framework: PydanticAI
27:54 - Resources
32:36 - Summary
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