Agentic AI: Build Your First Agentic AI System

Course details

Dive into agentic AI and master the skills you need to build scalable, real-world systems within an enterprise environment. In this course, industry expert Aishwarya Naresh Reganti shows you how to identify suitable business problems for agentic AI solutions, break down systems, iterate designs from baseline to advanced setups, and evaluate design trade-offs. Learn how to apply a comprehensive CCCD (Continuous Calibration Continuous Development) framework to safely increase AI autonomy while maintaining user trust. Tackle complex AI challenges as you gain insights on guardrails, governance, and security. This course is suited for engineers, product managers, enterprise leaders, and anyone eager to move beyond AI basics and implementation hurdles. Whether you're enhancing your existing skills or seeking to refine AI systems from concept to execution, this course delivers valuable knowledge for practical applications.

Learning objectives
Identify and scope a business problem that is suitable for agentic AI.
Break down a system into key components such as retrieval, reasoning, tools, and memory.
Iterate on design versions, moving from a baseline system to more advanced multi-agent or reasoning-augmented setups.
Evaluate trade-offs between complexity, cost, and reliability when making design choices.
Produce a concrete system plan that they can bring back and implement in their own work environment.

Concepts

Introduction

Building the right way - Launch your agentic AI journey

Your Next Agentic AI Project

Identifying agentic AI opportunities
Start planning for your AI customer support agent
Design levels of autonomy for customer support

Agentic AI Lifecycle

Building the baseline agentic system
Continuous calibration continuous development (CC CD)
Continuous development (CD) deep dive - The iterative development phase
Continuous calibration (CC) deep dive - The iterative calibration phase

Building the Baseline Agentic System

Add simple reasoning for action autonomy with baseline system testing
Implement baseline system calibration (CC)
Implement baseline system update (CD)

Enhancing Your AI Agent with Tools and Memory

Implement basic retrieval
Error analysis and metric design
Version two system evaluation

Production Considerations and Planning

Cost-complexity-reliability trade-offs
Guardrails, governance, and security
Roadmap creation - from concept to production-ready AI system

Conclusion

From baseline to autonomy - your ongoing agentic AI journey