Improve your customer experience with AI agents. Learn how to design and deploy intelligent AI agents that are capable of reasoning, planning, and taking action in real-time. Build your own autonomous customer assistant while exploring cutting-edge tools like LLMs, RAG, LangChain, and more.
Understand what agentic AI is, how it differs from generative AI, and its role in customer interactions
Building and deploying an autonomous AI Agent
Dive into the technical components and start building a simple agentic AI
Focus on autonomy, planning, and scalability in agentic AI systems
This course teaches participants how to design, build, and deploy autonomous agentic AI systems for customer interactions. Through hands-on labs and a capstone project, students will learn to use LLMs, RAG, memory, and APIs to create AI agents that can reason, act, and plan. The course also covers deployment, ethical concerns, and real-world applications in support, sales, and service environments.
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Day One: Foundations of Agentic AI
Introduction to Agentic AI
Definition and core chrematistics
Autonomy, reasoning, action-oriented
Evolution from generative AI to Agentic AI
Key use cases in customer-facing roles (i.e. support, sales, engagement)
Interactive Q&A: What makes AI “Agentic”
Technology Stack Overview
Large Language Models (LLMs) for reasoning and communication
Retrieval-Augmented Generation (RAG) for real-time access
APIs and integrations for action-taking
Memory and context management systems
Demo: Compare a generative chatbot vs. an agentic AI workflow
Customer Interaction Scenarios
Proactive vs. reactive AI: Examples in e-commerce, healthcare, and finance
Mapping customer journeys to agentic AI capabilities
Group Activity: Brainstorm a customer interaction problem agentic AI could solve
Setting the Stage
Tools and platforms
i. LangChain, Hugging Face, API frameworks, etc. - Course project intro: Build an agentic AI customer assistant - Hands-on Lab: Set up development environment (Python, API’s, LLM access)
Day Two: Technical Foundation and Building Blocks
LLMs and Reasoning
How LLMs power natural language understanding and generation
Prompt engineering for goal-oriented tasks
Hands-on Lab: Create a basic LLM-powered Q&A system
Adding Context with RAG
What is Retrieval-Augmented Generation?
Connecting LLMs to external data sources
Hands-On Lab: Build a RAG system to fetch real-time order status
Action Capabilities
Integrating APIs for task execution
Designing multi-step workflows
Hands-on Lab: Add an API call to reschedule a mock delivery
Wrap-up and Project Work
Combining Day 2 skills into a mini-agent
Group Discussion: Challenges in autonomy and decision making
Project Time: Begin Coding the customer assistant (i.e. order support agent
Day Three: Designing Autonomous Agents
Autonomy and Planning
How agentic AI breaks down goals into actionable steps
Algorithms for planning (i.e. tree search reinforcement learning basics
Demo: An agent resolving a multi-step customer query
Memory and Context Management
Short-term vs. long-term memory in AI Agents
Maintaining conversation context across interactions
Hands-on Lab: Add memory to your agent for follow-up questions
Scaling Agentic AI
Handling high volumes of customer interactions
Load balancing and performance optimization
Case Study: A real-world deployment
Project Development
Refine the customer assistant: Add planning and memory features
Peer Review: Share progress and Troubleshoot
Day 4: Business Applications and Deployment
Business Use Cases
Customer support: Ticketing, refunds, escalations
Sales: Upselling, personalized recommendations
Proactive care: Anticipating customer needs
Group Activity: Design an agentic AI for a specific industry
Integration with Business Systems
Connecting to CRMs (e.g., Salesforce), ERPs, and messaging platforms
Security and data privacy considerations
Hands-On: Simulate integration with a mock CRM
Deployment Strategies
Cloud vs. on-premise deployment
Monitoring and maintaining AI agents
Demo: Deploy a sample agent to a cloud platform
Project Refinement
Finalize the customer assistant: Test multi-step workflows
Prepare for Day 5 presentations
Day 5: Ethics, Evaluation, and Capstone
Ethics and Governance
Bias and fairness in autonomous decision-making
Transparency: Letting customers know they’re interacting with AI
Guardrails and human oversight
Discussion: Ethical dilemmas in customer-facing AI
Evaluating Agentic AI
Metrics: Accuracy, customer satisfaction, task completion rate
Testing for edge cases and failure modes
Hands-On: Test your agent with simulated customer scenarios
Capstone Project Presentations
Teams present their customer assistants (e.g., demo + explanation)
Feedback from peers and instructors
Wrap-Up and Next Steps
Recap of key learnings
Resources for further study (e.g., frameworks, research papers)
Q&A and course feedback
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier