Agentic AI Course - in Philadelphia

Agentic AI Training Course Outline

Module 1: Agentic AI Foundations and Use-Case Selection

• What is Agentic AI
• Agents vs Chatbots vs RPA
• The Agent Loop: Perceive, Plan, Act, Reflect
• Anatomy of an Agent
• Role of LLMs
• Use-Case Selection Framework
• Cost and Latency Basics
• Real-World Examples

Hands-On Lab: Run, Break, and Observe

Module 2: LLMs, Prompts, Memory and Tools

• LLM Essentials
• Token Economics
• Prompt Engineering
• Structured Outputs
• Short-Term and Long-Term Memory
• Tool Calling
• Tool Categories
• Reducing Hallucinations

Hands-On Lab: Memory and Real Data 

Module 3: Building Your First Single Agent

• AI Workflows vs Agents
• Orchestration Patterns
• Planner-Executor Pattern
• Reflection and Self-Correction
• Vector Databases
• Retrieval-Augmented Generation (RAG)
• Model Context Protocol (MCP)
• CrewAI Walkthrough
• LangGraph Overview
• Limitations of Single-Agent Systems

Hands-On Lab: Build the Research Agent

Module 4: Debugging Agents and Multi-Agent Systems

• Debugging:
• Seven Common Failure Modes
• Reading Logs and Traces
• Prompt Engineering for Agents
• Validation Layers and Retries
• Multi-Agent:
• Why Multi-Agent Systems
• Role-Based Agents
• Supervisor-Worker Pattern
• Sequential, Parallel, Hierarchical Topologies
• Agent Delegation and Handoff
• Feedback and Evaluation Loops

Hands-On Lab: Break, Repair and Build the Team

Module 5: Connecting Agents to Real Business Systems

• Agentic Workflows vs Traditional Automation
• Integrating External APIs
• Email and Slack Integration
• Reading Internal Data Sources
• Customer Support Agent Example
• Human-in-the-Loop Design
• Human Oversight and Accountability

Hands-On Lab: Connected Pipeline

Module 6: Capstone — Deploying a Production-Ready Agent

• Scoping a Capstone Use Case
• Designing Agent Architectures
• Cloud Deployment
• Monitoring in Production
• Guardrails and Validation
• Responsible AI
• AI Governance in Agentic Systems
• Risk Management
• Framework Landscape

Hands-On Lab: Capstone Project and Deployment
 

Who Should Attend this Agentic AI Course?

This Agentic AI Training is suitable for professionals seeking practical knowledge of AI agents, workflow automation, and intelligent AI systems. The course is ideal for delegates looking to build AI-driven solutions, automate business processes, and strengthen modern AI development skills. It is particularly beneficial for:

• Software Developers
• AI Engineers
• Data Analysts
• DevOps Engineers
• Technical Project Managers
• Automation Engineers

Prerequisites for the Agentic AI Training

There are no formal prerequisites for attending this Agentic AI Course. However, basic familiarity with Python programming, APIs, and command-line tools will be beneficial for understanding practical labs and AI workflow exercises.

Agentic AI Course Overview

Agentic AI Course introduces delegates to intelligent AI agents, workflow automation, and modern AI engineering concepts. The training covers prompt engineering, memory systems, tool calling, Model Context Protocol (MCP), and multi-agent architectures.

Delegates will strengthen practical AI engineering, workflow automation, and orchestration skills used in modern organisations. The course helps professionals improve problem-solving, automation design, API integration, and intelligent system deployment capabilities.

This 2-Day course offered by The Knowledge Academy enables delegates to design, build, and deploy production-ready AI agents confidently. Delegates will gain hands-on experience using frameworks such as CrewAI and LangGraph for business automation and intelligent workflows.

Agentic AI Course Objectives:

• To understand Agentic AI foundations and orchestration principles
• To learn prompt engineering and memory management techniques
• To build AI agents using CrewAI and LangGraph frameworks
• To implement RAG and Model Context Protocol (MCP) workflows
• To integrate AI agents with APIs and external business systems
• To design multi-agent systems with delegation and supervision models
• To debug AI workflows using validation and evaluation techniques
• To deploy production-ready AI agents using cloud environments

After completing this training, delegates will be able to build intelligent AI agents and automate real-world workflows confidently. Delegates will also gain practical skills in AI orchestration, API integration, and multi-agent collaboration. This training further helps delegates apply responsible AI practices and deploy AI-driven solutions in business environments.

What’s Included in this Agentic AI Course?

• World-Class Training Sessions from Experienced Instructors
• Agentic AI Certificate
• Digital Delegate Pack