Title Page Building Agentic Workflows: From Simple Chains to Autonomous AI Teams Subtitle: A Step-by-Step Guide for Developers, AI Engineers, and Automation Architects Includes: Python code examples, LangChain & CrewAI templates, and production-ready patterns
Table of Contents Introduction: What Are Agentic Workflows?
Why traditional automation fails with complex tasks Defining agentic vs. deterministic workflows The 3 pillars: Memory, Tools, and Planning Real-world ROI: Customer support, research, data extraction
Chapter 1: Core Concepts & Architecture
Agents – LLM-powered decision makers Tools – APIs, calculators, search, databases Orchestration – Sequential, parallel, or dynamic routing Memory – Short-term (conversation) vs. long-term (vector DB) Human-in-the-loop – Approval, escalation, feedback
Chapter 2: Designing Your First Agent
Choosing the right LLM (GPT-4o, Claude 3.5, Llama 3, Mistral) Writing system prompts for tool use and reasoning Implementing ReAct, CoT, and Plan-and-Solve patterns Code example: A basic research agent with web search building agentic workflows pdf free download
Chapter 3: Workflow Patterns
Chain-of-Thought (CoT) – Step-by-step reasoning Router/Classifier – Dynamic path selection Map-Reduce – Parallel processing of subtasks Reflection & Self-Correction – Multi-turn refinement Multi-Agent Collaboration – Debate, voting, role assignment Graph-based workflows (LangGraph, AutoGen)
Chapter 4: Tools & Frameworks (Hands-On) Title Page Building Agentic Workflows: From Simple Chains
LangChain & LangGraph – Stateful, cyclic workflows CrewAI – Role-based agent teams AutoGen – Conversational agents from Microsoft DSPy – Optimizing prompts programmatically Custom tools – Building API wrappers and Python functions
Chapter 5: Memory & State Management