Certified Master in Agentic AI: A 52-Week Applied Program
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Unclaimed ProfileThe Certified Master in Agentic AI: A 52-Week Applied Program by the School of AI represents a groundbreaking, year-long journey designed for software engineers, AI practitioners, and technology leaders aiming to specialize in the rapidly evolving field of autonomous AI agents. Over the course of 52 weeks, students dive deep into the practical implementation of Agentic AI, moving far beyond theoretical foundations. The comprehensive curriculum explores cutting-edge multi-agent orchestration frameworks including LangChain, LangGraph, CrewAI, AutoGPT, and LlamaIndex. Participants learn how to design, build, and deploy sophisticated AI agents capable of reasoning, planning, and executing complex workflows with minimal human intervention. Key areas of focus include setting up robust Retrieval-Augmented Generation (RAG) pipelines with advanced vector databases like Pinecone, FAISS, and Chroma, as well as mastering memory retention, tool usage, and human-in-the-loop governance. By emphasizing hands-on application, the program empowers professionals to automate enterprise operations, integrate AI tools with existing APIs, and architect resilient, secure, and cost-effective multi-agent ecosystems that deliver real-world business value.
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School of AI
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School of AI is an elite online training collective dedicated to delivering highly practical, cutting-edge education in artificial intelligence, generative AI, and autonomous agent development. Recognizing that theory alone is insufficient in the rapidly evolving tech landscape, the School of AI structures its curriculum entirely around hands-on, project-based learning. Across a comprehensive range of programs—spanning multi-week masterclasses, deep-dives into modern tools like Anthropic Claude, Claude Code, and DeepSeek R1, and intense multi-project bootcamps—the institution ensures that students bridge the gap between academic AI concepts and production-ready deployments. Through a rigorous pedagogical approach that includes hundreds of practical labs and real-world…Show more
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Program Overview
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Learning format
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Price
Price may change · updated within 1–2 weeks
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What You'll Learn
- Build and deploy AI agents using LLMs, memory, tools, and reasoning strategies across real-world business and technical domains.
- Master Agentic AI frameworks like LangChain, LangGraph, CrewAI, AutoGPT, BabyAGI, and LlamaIndex for hands-on applications.
- Implement multi-agent systems with communication protocols, task delegation, and emergent behavior for complex workflows.
- Apply Retrieval-Augmented Generation (RAG) with vector databases like FAISS, Pinecone, and Chroma to boost agent performance.
- Integrate AI agents with enterprise systems, APIs, workflow tools, cloud platforms, and IoT for real-world automation.
- Ensure AI safety and governance with guardrails, human-in-the-loop feedback, adversarial defense, and ethical design.
- Optimize AI agent performance by tuning memory, reducing latency, cutting costs, and evaluating ROI with advanced metrics.
- Deliver a capstone project where you design, deploy, and present a full-scale Agentic AI solution with practical business impact.
Best For
- Software engineers and AI practitioners looking to transition from basic prompt engineering to complex agent orchestration.
- Technical leads and system architects responsible for integrating autonomous workflows into enterprise environments.
- Developers proficient in Python who want to build modular, scalable, and memory-persistent multi-agent systems.
- Professionals aiming to master advanced RAG pipelines and tool-use automation using modern frameworks.
Not For
- Absolute beginners with no programming experience or familiarity with Large Language Model fundamentals.
- Casual learners seeking a quick high-level overview or theory-only introduction to AI.
- Individuals looking for a short weekend workshop rather than a long-term, intensive year-long professional commitment.
- Data scientists focused exclusively on model training and fine-tuning rather than agentic application development.
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