Agentic AI Bootcamp: Build Autonomous AI Systems in 3 Days

Name: Agentic AI Bootcamp: Build Autonomous AI Systems in 3 Days
Rating: 3.2 (3 reviews)

Design, build, and deploy production-ready multi-agent AI systems with tools, memory, and real workflows

Created bySchool of AI

Last updated 4/2026

English

English [Auto],

What you'll learn

Understand how Agentic AI systems work (agents, reasoning, tools, memory)
Build single-agent systems with tool usage and retrieval-based memory
Design and implement multi-agent architectures for complex workflows
Use frameworks like LangGraph, CrewAI, and AutoGen for orchestration
Integrate RAG (Retrieval-Augmented Generation) for grounded, accurate responses
Connect agents to real-world systems via APIs and automation tools (Zapier, n8n)
Implement evaluation pipelines (LLM-as-a-Judge, test cases, regression testing)
Add guardrails and safety mechanisms to prevent failures and misuse
Monitor systems with logging, observability, and performance tracking
Design scalable, production-ready AI architectures for enterprise use cases

Course content

4 sections • 23 lectures • 3h 55m total length

Session 1: What is Agentic AI (Really)?12:13
Explore what agentic AI really means, distinguishing agents from chatbots and detailing how reasoning, planning, tool use, memory, and the REACT loop enable autonomous, goal-driven action.
Certificate of Completion0:27
Complete the course on Udemy to receive a certificate of completion from the School of AI. Email your Udemy certificate to vivian at schoolofaiglobal.com for verification and an official certificate.
Session 2: Anatomy of an AI Agent9:07
Design agents by configuring the system prompt, memory, tools, and output structure to transform a model into a reliable, interactive system.
Session 3: Single-Agent Architecture12:22
Explore single-agent architecture from input to output and learn to design reliable, probabilistic AI systems with guardrails, validation, and clear inputs.
Session 4: Tools & Function Calling11:29
Learn how tools and function calling empower autonomous AI agents to act, via APIs, data, and computation, with structure, guardrails, and observability for real-world reliability.
Session 5: Memory Systems11:57
Build effective AI agents by designing memory systems that move from stateless to stateful, combining short-term and long-term memory, retrieval, and vector databases for personalized, multi-step workflows.
Hands-On Lab: Build Your First AI Agent6:19

Session 1: Why Single Agents Break11:17
Single-agent systems crumble as problems grow, due to context overload, tool confusion, and lack of specialization, yielding inconsistent, unreliable results. Shift to multi-agent systems with specialized roles.
Session 2: Multi-Agent Design Patterns13:45
Explore multi-agent design patterns to structure roles, interactions, and workflows for reliable, scalable systems. Learn patterns like planner-executor, researcher-writer, critic-improver, and supervisor-worker to organize tasks, reduce chaos, and enable collaboration.
Session 3: Orchestration Frameworks13:22
Learn how orchestration coordinates multiple agents, defines task flow and state across frameworks like Langraph, Crew AI, and Autogen, and choose the right approach for your production multi-agent system.
Session 4: Designing Agent Workflows12:48
Design agent workflows by decomposing tasks, assigning clear roles, and shaping communication and state passing to coordinate multi-agent systems, reduce redundancy, and scale effectively.
Session 5: Memory + Context at Scale12:05
Explore how to design memory and context at scale for autonomous agents, balancing shared and private memory, retrieval, and compression to manage context windows and long-running workflows.
Hands-On Lab: Build a Multi-Agent System8:04

Session 1: Evaluation & Reliability11:55
Learn how evaluation and reliability separate demos from production systems, using a large language model as a judge, test cases, regression, and evaluation to prevent hallucinations and workflow failures.
Session 2: Guardrails & Safety11:58
Implement defense-in-depth guardrails to ensure safe, reliable AI agents in production by applying input validation, output validation, tool usage constraints, and human oversight, while guarding against prompt injection.
Session 3: Observability & Monitoring9:48
Explore observability and monitoring to see what agent systems, including multi-agent workflows, do in production, tracing inputs, outputs, workflow steps, and latency for debugging and scaling.
Session 4: Scaling Agent Systems10:48
Scale agent systems by balancing cost, speed, and quality through cost optimization, parallelization, workflow optimization, and caching, enabling real-world deployment.
Session 5: Enterprise Architecture9:50
Design enterprise-grade ai systems with secure, scalable, production-ready architectures spanning api, agent, orchestration, and infrastructure layers. Integrate with existing systems and enforce security, governance, and data boundaries.
Capstone Project: Build a Production-Ready Agent System11:38

Agent + RAG deep dive10:32
Explore how Retrieval Augmented Generation (RAG) powers agents with up-to-date, domain-specific knowledge by retrieving context, injecting it into prompts, and producing accurate, grounded responses for enterprise tasks.
Agent + automation (Zapier / n8n)7:45
Combine agents with automation tools like Zapier and N8n to turn reasoning into real-world actions. Design triggers and actions, connect via APIs, and build workflows with guardrails for safe automation.
Agent + enterprise APIs8:30
Agents connect to enterprise APIs to act on real systems, using REST, GraphQL, and microservices. Design safe, well-documented APIs with authentication, logging, and orchestration to automate business workflows.
Agent + UI (Streamlit)7:49
Turn agent systems into usable products with a Streamlet-based user interface, enabling chat, file uploads, and workflow visuals connected to back-end agents.

Requirements

Basic understanding of AI concepts (helpful but not required)
Familiarity with Python is useful, but not mandatory
Comfortable using web apps and APIs (beginner level is enough)
Laptop with internet connection (Mac/Windows/Linux)
Ability to install tools like: Python and VS Code (or any IDE)
Willingness to learn hands-on by building real systems
No prior experience with agents or LLM frameworks required
Curiosity about AI, automation, and real-world applications

Description

“This course contains the use of artificial intelligence”

Master Agentic AI: From Foundations to Production-Ready Systems

This intensive 3-day course is designed to take you from understanding the basics of Agentic AI to building production-ready multi-agent systems that can operate in real-world business environments. You will go beyond simple prompting and learn how to design intelligent systems that can reason, act, use tools, and collaborate to complete complex tasks.

On Day 1, you will build a strong foundation in how AI agents actually work. You will learn the difference between chatbots, workflows, and agents, and understand core concepts like the ReAct loop (Think → Act → Observe). You will then dive into the anatomy of an AI agent, including system prompts, memory, tools, and output structures. By the end of the day, you will build your first working agent with tool integration and retrieval-based memory.

Day 2 focuses on scaling from single agents to multi-agent systems. You will explore powerful design patterns such as Planner–Executor, Researcher–Writer, and Critic–Improver, and understand why single agents fail in real-world scenarios. You will learn how to use modern orchestration frameworks like LangGraph, CrewAI, and AutoGen to design structured workflows. Through hands-on implementation, you will build a multi-agent system that collaborates across roles to solve end-to-end tasks.

On Day 3, you will transition from prototypes to production-ready AI systems. You will learn how to implement evaluation pipelines, including LLM-as-a-Judge, test cases, and regression testing. You will design guardrails and safety systems to prevent prompt injection, unsafe outputs, and tool misuse. You will also explore observability and monitoring, tracking inputs, outputs, latency, and failures. Finally, you will learn how to scale agent systems using cost optimization, parallelization, and caching, and design enterprise-grade architectures with API layers, orchestration layers, and governance.

Throughout the course, you will also dive into advanced topics like Agent + RAG (Retrieval-Augmented Generation) for knowledge grounding, Agent + automation (Zapier, n8n) for workflow execution, Agent + enterprise APIs for real-world integration, and Agent + UI (Streamlit) for building interactive applications.

The course culminates in a capstone project, where you will build a production-ready multi-agent system with tools, memory, evaluation, and guardrails—complete with a working demo and business use case.

By the end, you will not just understand Agentic AI—you will be able to design, build, and deploy scalable, reliable, and enterprise-ready AI systems that deliver real impact.

Who this course is for:

Professionals looking to build real-world AI systems, not just use prompts
Software engineers who want to learn agentic AI and multi-agent architectures
Product managers aiming to design AI-powered products and workflows
Data scientists transitioning into LLM-based and agent-based systems
Entrepreneurs and builders creating AI startups or automation tools
Automation and no-code users wanting to upgrade to intelligent AI systems
Technical leaders exploring enterprise AI architecture and strategy
Anyone interested in moving from AI experimentation → production-ready systems

Agentic AI Bootcamp: Build Autonomous AI Systems in 3 Days

What you'll learn

Explore related topics

Course content

DAY 1 — Foundations + Build Your First AI Agent7 lectures • 1hr 4min

DAY 2 — Multi-Agent Systems + Orchestration6 lectures • 1hr 11min

DAY 3 — Production Systems + Enterprise Readiness6 lectures • 1hr 6min

Extra Modules4 lectures • 35min

Requirements

Description

Who this course is for: