Designing and Implementing Agentic AI Solutions is an advanced, hands-on course focused on building autonomous AI agents that can plan tasks, make decisions, and execute complex workflows using Large Language Models (LLMs). The course teaches how modern agent frameworks enable AI systems to move beyond simple prompts into intelligent, multi-step automation.

Learners gain practical experience building AI agents that integrate tools, APIs, and databases, and develop enterprise-ready chatbots using Retrieval-Augmented Generation (RAG) and vector databases. Through real-world projects, participants learn how to design and implement agentic AI solutions for intelligent automation and scalable applications.

By the end of the course, learners will be equipped to design and deploy agent-based AI systems for real-world business and technology use cases.

This is an advanced-level course designed for learners who already have experience working with Generative AI and Large Language Models (LLMs). To successfully complete this course, participants should have:

• Prior experience using ChatGPT or similar LLM-based tools

• Understanding of prompt engineering concepts and AI workflows

• Basic knowledge of Python programming

• Familiarity with APIs, REST services, or function calling concepts

• Experience working with data sources such as files, databases, or SQL (basic level)

• Strongly recommended (but not mandatory):

• Completion of Prompt Engineering for AI and ChatGPT Training or Mastering ChatGPT and Generative AI Tools

• Exposure to cloud platforms or AI frameworks

• Understanding of basic software development workflows

This course is ideal for professionals who want to move from using AI tools to designing autonomous, production-ready AI systems.

By the end of this course, you will be able to:

• Understand the core principles of Agentic AI and how autonomous agents differ from prompt-based AI systems

• Design agent architectures that support task planning, decision-making, and tool execution

• Build AI agents using LangChain, OpenAI Function Calling, and AutoGen

• Implement tool calling, API integration, and SQL-based data access within AI agents

• Design and automate multi-step workflows using agent orchestration techniques

• Develop LLM-based chatbots for real-world and enterprise use cases

• Implement Retrieval-Augmented Generation (RAG) for context-aware AI responses

• Work with vector databases such as Pinecone, FAISS, and Chroma for semantic search

• Build enterprise knowledge assistants that combine LLM reasoning with external data sources

• Design and manage multi-agent systems for collaboration and task delegation

• Apply guardrails, safety controls, and human-in-the-loop strategies to improve reliability

• Identify and mitigate risks such as hallucinations, prompt injection, and security issues

• Deploy and scale agentic AI solutions for production environments

• Apply best practices for monitoring, performance optimization, and cost control

• Design end-to-end agentic AI solutions for real-world business and technical scenarios

Completing this course prepares learners for advanced roles focused on designing, building, and deploying autonomous AI systems and intelligent workflows. After completing the training, learners will be better prepared for positions such as:

• AI Engineer (Agentic AI / LLM-based Systems)

• Generative AI Engineer

• Agentic AI Architect

• Machine Learning Engineer (Applied / Systems-focused)

• AI Automation Engineer

• LLM Application Engineer

• AI Solutions Engineer

• AI Platform Engineer

• Data & AI Product Engineer

• AI Technical Lead / AI Solutions Lead

Module 1: Foundations of Agentic AI

• What is Agentic AI and how it differs from traditional AI systems

• Agents vs prompts vs workflows

• Role of agents in autonomous and semi-autonomous systems

• Real-world examples of agentic AI in enterprises

• Use cases: automation, decision support, orchestration

Module 2: Core Components of Autonomous Agents

• Agent architecture and lifecycle

• Task planning and goal decomposition

• Decision-making and reasoning strategies

• Tool usage and action execution

• Memory, state, and context management

Module 3: Agent Frameworks and Ecosystem

• Overview of popular agent frameworks

• LangChain agents and tools

• OpenAI function calling

• AutoGen for multi-agent collaboration

• Choosing the right framework for a use case

Module 4: Building AI Agents (Hands-On)

• Designing single-agent workflows

• Implementing task-oriented agents

• Tool calling and function execution

• Error handling and fallback strategies

• Hands-on lab: Task-execution AI agent

Module 5: Tool, API, and Database Integration

• Integrating REST APIs with AI agents

• Using external tools and services

• SQL integration for data access and analysis

• Secure handling of credentials and keys

• Hands-on lab: Data analysis agent with tools and SQL

Module 6: Workflow Automation with Agentic AI

• Designing multi-step autonomous workflows

• Agent orchestration and coordination

• Chaining agents for complex tasks

• Monitoring and controlling agent behavior

• Use cases: reporting, analysis, operational automation

Module 7: LLM-Based Chatbot Development

• LLM chatbots vs rule-based chatbots

• Conversational design for enterprise use

• Context management and conversation memory

• Building scalable chatbot architectures

Module 8: Retrieval-Augmented Generation (RAG)

• What is RAG and why it matters

• Document ingestion and chunking strategies

• Embeddings and semantic search

• Context-aware response generation

Module 9: Vector Databases for Agentic Systems

• Introduction to vector databases

• Pinecone, FAISS, and Chroma overview

• Indexing and querying embeddings

• Optimizing retrieval for accuracy and performance

• Hands-on lab: Knowledge-base retrieval system

Module 10: Enterprise Knowledge Chatbot (Project)

• Designing an enterprise knowledge assistant

• Integrating RAG with LLM chatbots

• Using agents to answer complex queries

• Handling updates, versioning, and scalability

• Hands-on project: Enterprise knowledge chatbot

Module 11: Multi-Agent Systems

• Single-agent vs multi-agent architectures

• Agent-to-agent communication

• Collaboration, delegation, and conflict resolution

• AutoGen-based multi-agent workflows

Module 12: Reliability, Safety, and Control

• Managing hallucinations and errors

• Guardrails and response validation

• Prompt injection and security risks

• Human-in-the-loop strategies

Module 13: Deployment and Scaling Agentic AI

• Deploying agentic AI solutions

• Performance considerations

• Cost optimization and monitoring

• Integrating agentic AI into existing systems

Module 14: Real-World Use Cases

• Review of real-world enterprise implementations

• Designing an end-to-end agentic AI solution

• Best practices and architectural patterns

• Final capstone discussion and solution walkthrough