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Part of Agentic AI Developer

Certification Cost

Course Description

Agentic AI Developer Course Overview

Agentic AI Developer Training for Software Engineers is a 60-hour, beginner-friendly program designed to help learners build end-to-end AI agent workflows, from single intelligent agents to enterprise-grade multi-agent systems. This course focuses on practical implementation of agent architectures, including planning logic, tool integration, short-term and long-term memory design, structured outputs, and workflow automation. Learners progressively build a complete system consisting of a Supervisor Agent, NLP Agent, Database Agent, Reporting Generation Agent, and Email Notification Agent. You will learn how agents break down goals into tasks, select tools safely, retrieve and validate structured data, generate business-ready reports, and automate email-based notifications with attachments. The course also introduces Model Context Protocol (MCP) to standardize tool and data connectivity within agent ecosystems. Through guided hands-on labs in every module, learners build and integrate a fully functional multi-agent automation pipeline. By the end of this course, you will confidently design, test, debug, and present an enterprise-style Agentic AI system suitable for production-style environments.

QUICK FACTS

Agentic AI Developer Curriculum

Concepts

     What is Agentic AI vs chatbot AI

     Where agents are used in software engineering

     What you’ll build in this course (multi-agent pipeline)

Lab

     Run and observe a sample agent workflow

     Identify “goal → plan → action → output” in a guided demo

Concepts

     Tokens, context, prompts (simple explanation)

     Why LLMs fail (hallucinations, ambiguity)

     How agents reduce errors with tools + steps

Lab

     Compare normal prompts vs structured prompts

     Create a “task checklist prompt” for reliability

Concepts

     Role prompts, constraints, output formats

     Task decomposition and chaining

     Consistency with templates

Lab

     Build an agent-style prompt template

     Validate with 3 different use cases

Concepts

     Agent components: planner, tool layer, memory, output

     Stateless vs stateful agents

     Observability: logs and trace thinking (beginner approach)

Lab

     Draw your agent architecture

     Simulate agent loop steps using a guided worksheet

Concepts

     Goal-based planning

     ReAct-style thinking (simplified)

     Guardrails: what agents must NOT do

Lab

     Build a basic planning agent

     Add “stop conditions” and “fallback response”

CAREER GROWTH

Your Career Path

Climb the ladder of success with structured role progression.

1

Agentic AI Developer

Step 1
2

AI Automation Engineer

Step 2
3

LLM Application Developer

Step 3
4

AI Workflow Engineer

Step 4
5

AI Solutions Engineer

Step 5

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