Learn how to build, deploy, and optimize AI agents with our comprehensive step-by-step guides. From beginners to advanced practitioners.
A practical, phase-by-phase guide to deploying AI agents inside a company. Covers use case selection, MVP scoping, production hardening, governance, and infrastructure options with working Python examples.
A practical framework for calculating and tracking the ROI of AI agent deployments. Covers time savings, cost reduction, and revenue impact formulas, with real examples and a monthly tracking template.
A practical guide to the three approaches for teaching an AI agent about your data: RAG with vector databases, OpenAI fine-tuning, and context injection. Includes full working Python code for each approach.
Complete LangGraph tutorial covering StateGraph, conditional routing, and human-in-the-loop checkpoints. Build three progressively complex multi-agent workflows with full working Python code.
Build an AI recruitment agent that screens resumes, matches candidates to roles, automates interview scheduling, and ensures compliance — with practical implementation examples.
A step-by-step beginner's guide to creating your first AI agent using no-code tools. Learn the fundamentals and build a working agent in under an hour.
Build an AI customer service agent that triages tickets, searches your knowledge base, resolves common issues, and escalates complex cases intelligently.
Build an AI agent that automates sales workflows — from lead qualification and personalized outreach to CRM updates and pipeline management. Includes ROI framework.
Master multi-agent system design with proven orchestration patterns, communication protocols, conflict resolution strategies, and production scaling techniques.
Learn to build conversational multi-agent systems with Microsoft AutoGen. Covers agent types, human-in-the-loop workflows, group chat, and code execution patterns.
Learn how to build multi-agent AI systems with CrewAI. Define agent roles, delegate tasks, and orchestrate collaborative workflows with practical Python examples.
Step-by-step guide to building a functional AI agent with LangChain in Python. Covers tools, ReAct agents, memory, chains, and deployment best practices.
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