Mastering AI Agents: From Fundamentals to Deployment

  • Course Duration 3 Weeks
  • Course Mode Instructor Led Online Training
  • Date & Time August 20, 2025

About The Course

This course, offered by AiCouncil in association with the NVIDIA Inception Program, Microsoft Solution Partner, and AWS Partner Network, provides a comprehensive journey into the world of Agentic AI. You'll explore the foundational concepts of AI agents, large language models, and essential technologies like function calling and vector databases. Through hands-on projects, you'll learn to build and deploy intelligent agents using modern frameworks. The curriculum also delves into advanced topics such as monetizing AI agents, creating custom AI assistants akin to Microsoft Copilot, and leveraging open-source solutions. Furthermore, it addresses critical considerations around security, privacy, and ethical practices in AI, preparing you to develop responsible and impactful agentic AI solutions.

Key Features

Instructor-Led, Interactive Training

Live, expert-led sessions for hands-on learning

Lifetime Access to Recordings

Revisit recorded classes anytime, at your convenience

Assignments & Real-Time Projects

Apply skills through practical projects after every module

Lifetime Job Assistance

Ongoing support for AI and Data Science job opportunities

3 Years of Technical Support

24/7 query resolution and technical help for 3 years

Globally Recognized Certification

Certified by AiCouncil, backed by Microsoft & AWS

Highlights

Master Generative AI Models – Understand transformers, BERT, and GPT for real-world NLP use cases like sentiment analysis and content generation.

Build Powerful AI Agents – Learn to design, develop, and deploy intelligent AI agents using leading frameworks like Flowise, LangChain, LangGraph, AutoGen, and CrewAI.

Master LLMs & Function Calling – Go deep into Large Language Models (LLMs) like GPT, Gemini, and Llama, and discover how function calling enables agents to interact with external tools and APIs.

Implement Advanced RAG Systems – Understand Vector Databases, Embeddings, and Retrieval-Augmented Generation (RAG) to create AI agents with enhanced knowledge and reduced hallucinations.

Develop Custom AI Solutions – Get hands-on with projects ranging from content generation and lead research to building your own Python-based Vision Copilot and privacy-focused local RAG chatbots.

Deploy & Monetize Your Agents – Learn practical strategies for hosting AI agents on cloud platforms like Render, integrating them into websites, and turning your creations into sellable solutions.

Explore Open-Source AI – Dive into the world of private AI by running open-source LLMs like Llama 3.1 and Mistral locally using Ollama, and integrate them into your agent workflows.

Address AI Security & Ethics – Gain crucial knowledge on jailbreaks, prompt injections, data poisoning, copyrights, and data privacy to build responsible and secure AI agents.

Capstone & Certification – Complete an industry-style project and earn credentials from AiCouncil, partnered with the NVIDIA Inception Program, Microsoft Solution Partner, and AWS Partner Network.

Course Agenda

  • What is Agentic AI?
    • Definition and importance in today’s AI landscape.
    • Evolution from traditional AI to agentic systems.
    • Course structure and learning objectives.
  • Understanding AI Agents
    • Characteristics: Autonomy, perception, decision-making, and action.
    • Real-world examples: From chatbots to complex autonomous systems.
  • Large Language Models (LLMs) Overview
    • Architecture, training, and capabilities of LLMs.
    • Comparison of major LLMs: GPT, Claude, Gemini, Llama, Mistral.
    • Role of LLMs as the "brain" of AI agents.
  • Function Calling in LLMs
    • Concept and importance in agentic workflows.
    • Syntax, parameters, return values.
    • Hands-on examples.
  • Embeddings, Vector Databases, and RAG
    • What are embeddings?
    • Using vector databases for storing and querying high-dimensional data.
    • RAG (Retrieval-Augmented Generation): Enhancing LLM capabilities with external data.
  • Popular Agentic AI Frameworks
    • Core agent components: Planning, memory, tools, action.
    • Overview of LangChain, LangGraph, AutoGen, CrewAI.
  • Function Calling with APIs
    • Basics of APIs for AI agents.
    • Designing effective API integrations.
  • Section Summary
    • Recap and knowledge check of key concepts.

Exercise 1: Exploring LLM Capabilities

Objective:

Interact with various LLMs to understand their strengths and weaknesses in different tasks (e.g., creative writing, factual recall, code generation).

Concepts Used:

LLM prompting, comparative analysis.

Steps:

  • Access demo environments or free tiers of multiple LLMs (e.g., ChatGPT, Gemini, Claude).
  • Provide the same prompts to each LLM and compare responses.
  • Document observations on response quality, style, and potential biases.

Exercise 2: Simple Function Calling Simulation

Objective:

Understand the concept of function calling by simulating a basic interaction where an LLM "calls" an external tool.

Concepts Used:

Function calling logic, mock API responses.

Steps:

  • Define a simple "tool" (e.g., a weather lookup, a calculator) with predefined responses.
  • Formulate prompts that would naturally lead the LLM to "use" this tool.
  • Manually simulate the LLM outputting a function call, and then integrate the tool's response.
  • Getting Started with Flowise
    • Goals of the hands-on project.
    • Introduction to Flowise as a visual interface.
  • Installing Flowise Locally
    • Prerequisites: Installing Node.js.
    • Step-by-step Flowise installation and common fixes.
  • Using Flowise Interface
    • Navigating UI: Nodes, tools, and logic.
    • Simplifying LangChain/LangGraph via visual design.
  • Agent 1: Creative Writer & Title Generator
    • Building a multi-tasking AI agent.
    • Designing effective prompts and workflows.
  • Agent 2: Social Media Strategy Assistant
    • Content generation for social platforms.
    • Prompt engineering techniques for structured responses.
  • Agent 3: Lead Researcher & Email Generator
    • Simulated function calling for web research.
    • Generating customized outreach emails.
  • Agent 4: Python Interpreter, Calculator & Memory
    • Using Python tools for logic and computation.
    • Local storage for agent memory.
  • Section Recap
    • Review of agent designs and tools.
    • Best practices for developing efficient AI agents.

Exercise 1: Build a Multi-Tool AI Agent with Flowise

Objective:

Create a visual AI agent using Flowise that performs multiple tasks such as content generation, question answering, and creative writing.

Concepts Used:

LangChain components via Flowise, tool integration, agent chaining, memory components, API orchestration.

Steps:

  • Set up Flowise and create a new agent
  • Add tools: LLM (OpenAI), Prompt Template, Document Loader, Output Parser
  • Build 2–3 use cases like: Content writing assistant (e.g., blog title + body), Product Q&A from knowledge base
  • Deploy the agent using Flowise frontend

Exercise 2: Function-Calling Agent with Calculator & Python Code Interpreter

Objective:

Build a custom agent that dynamically calls specific functions (e.g., calculator, Python executor) based on user query.

Concepts Used:

Function calling in OpenAI GPT, JSON schema, code execution from Python environment, memory-based routing.

Steps:

  • Define function schema for: calculator, Python code execution
  • Configure agent to parse user input and choose the right function
  • Examples: "What is 27% of 3800?", "Write Python code to find prime numbers from 1 to 100"
  • Show result from appropriate function and return final output

Exercise 3: Lead Generation AI Agent with Internet Search (Flowise + LangChain)

Objective:

Create a lead generation agent that performs basic research on a person or business and drafts a custom outreach email.

Concepts Used:

LLMs, memory, function calling, search tools, prompt chaining, text summarisation.

Steps:

  • Use a research tool plugin (DuckDuckGo, SerpAPI, or static dummy search for demo)
  • Prompt agent to search for a person/business
  • Extract useful data (name, role, company, recent work)
  • Generate a personalized email using GPT-4
  • Wrap in Flowise interface for input/output
  • Advanced Agent Capabilities
    • Custom tools, RAG integration, and executing in apps.
  • Real-World Applications
    • Business use cases: Customer service, marketing, analysis.
    • Designing and pitching a sellable AI agent.
  • Hosting and Deployment
    • Deploying chatbots on Render and other platforms.
    • Embedding agents in websites or standalone apps.
  • UI/UX & Branding
    • Enhancing user experience and interface.
    • Custom styling and link integration.
  • Sales & Marketing Strategies
    • Lead generation, pricing models, warranties.
    • Client communication and retention.
  • Section Summary
    • Recap of deployment and commercialization strategies.
  • Setting Up Development Tools
    • Installing VS Code, Git, and managing GitHub projects.
  • Hands-On Project: Build Your Own Vision Copilot
    • Coding an assistant with vision capabilities (simulated).
    • Exploring Python codebases for LLM integration.
  • Advanced Features & Tips
    • Voice interaction, prompt refinement, speed optimization.
    • Tips for recording demos and showcasing your agent.
  • Section Recap
    • Summary of custom copilot creation techniques.

Exercise 1: Deploying a Flowise Agent to a Cloud Platform

Objective:

Host a Flowise-built AI agent on a public cloud service (e.g., Render, Heroku) to make it accessible online.

Concepts Used:

Cloud deployment, port forwarding, environmental variables, CI/CD basics.

Steps:

  • Prepare your Flowise agent for deployment (e.g., export, containerize).
  • Set up an account on a chosen cloud platform (e.g., Render).
  • Follow the platform-specific steps to deploy your Flowise application.
  • Test the deployed agent from a public URL.

Exercise 2: Integrating an Agent into a Simple Webpage

Objective:

Embed your deployed AI agent's chat interface into a basic HTML webpage.

Concepts Used:

HTML, JavaScript, iFrames or API calls for embedding.

Steps:

  • Create a simple HTML file.
  • Use an `iframe` or integrate via JavaScript/API calls to display your agent's UI.
  • Customize basic styling (colors, branding) using CSS.
  • Why Go Open-Source?
    • Advantages of local/private AI models.
  • Working with Open-Source LLMs
    • Overview: Llama 3.1, Mistral, DeepSeek R1.
    • Installing and running models using Ollama.
    • Combining LangChain with local inference for RAG agents.
  • Performance Optimization
    • Using Groq API for faster inference.
    • Choosing the right model version for your use case.
  • Security Threats in AI Agents
    • Jailbreaks, prompt injections, data poisoning.
  • Legal & Ethical Guidelines
    • Copyrights of AI-generated content.
    • Protecting client and user data.
    • Designing agents with ethical safeguards.
  • Final Recap
    • Summarization of privacy, safety, and best practices.

Exercise 1: Set Up and Run a Local LLaMA 3.1 Agent

Objective:

Install and configure a local instance of the LLaMA 3.1 model and build a simple chatbot using it.

Concepts Used:

Open-source LLMs, local model deployment, inference APIs, resource management.

Steps:

  • Download and install Ollama or relevant LLaMA runtime
  • Load LLaMA 3.1 model locally
  • Build a simple chat interface to interact with the model
  • Test with example queries for knowledge retrieval or casual chat

Exercise 2: Build a Privacy-Focused RAG Chatbot with Flowise + Ollama

Objective:

Combine local LLM with a private knowledge base for document Q&A without sending data to the cloud.

Concepts Used:

Retrieval-Augmented Generation, vector databases, local embeddings, privacy in AI.

Steps:

  • Prepare a private document dataset (PDFs, docs)
  • Create embeddings locally and set up a vector store
  • Connect local LLaMA model with the vector store via Flowise
  • Build chatbot interface that answers queries from local docs only

Exercise 3: Ethical AI Scenario Analysis & Mitigation

Objective:

Analyze a given scenario involving AI bias or security threats, then propose ethical mitigation strategies.

Concepts Used:

AI ethics, bias in datasets, prompt injection, data poisoning, IP concerns.

Steps:

  • Given 2-3 real-world scenarios (e.g., biased hiring AI, prompt injection attack)
  • Identify the ethical challenges and risks
  • Suggest practical solutions or safeguards (e.g., data auditing, adversarial testing)
  • Present findings and recommendations in a report or presentation format

Projects

These projects are excellent starting points for understanding core Agentic AI concepts using visual tools like **Flowise**. They focus on direct interaction with **Large Language Models (LLMs)**, chaining simple steps, and generating text-based output within a user-friendly visual interface.

  • Objective: Develop an AI agent using Flowise that assists users in generating creative content, such as blog post ideas, article outlines, and catchy titles, based on simple inputs.
  • Key Skills: Flowise visual workflow design, foundational LLM prompt engineering, basic agent chaining, structured text generation.
  • Use Case: Empowers content creators, marketers, and writers to quickly ideate and draft initial content pieces, overcoming writer's block and streamlining their content production process.
  • Set up your Flowise development environment.
  • Design a Flowise workflow to generate creative content ideas from a topic.
  • Implement a separate chain within the agent to generate a list of catchy titles for a given content.
  • Combine these capabilities into a single, cohesive multi-tasking agent.
  • Test and refine prompts for diverse creative outputs and ensure desired formatting.
  • Objective: Design an AI agent using Flowise that generates brand-aligned social media posts, relevant hashtags, engaging captions, and short promotional content based on brief inputs about a product or campaign.
  • Key Skills: LLM prompt engineering for tone and style, content summarization, hashtag generation, Flowise agent design, understanding platform-specific content needs.
  • Use Case: Supports startups and marketing teams by automating the creation of consistent, high-quality social media content that matches a desired brand voice, saving time on daily content creation and improving engagement.
  • Create prompt chains in Flowise to generate social media posts in different tones (e.g., professional, witty, informative).
  • Implement a mechanism for the agent to suggest relevant hashtags and short, engaging captions.
  • Design agent workflows to adapt output styles for different platforms (e.g., LinkedIn, Instagram, Twitter).
  • Set up inputs for product descriptions or campaign goals, and outputs for formatted social media content.
  • Test the agent's ability to maintain a consistent brand voice across various content types.

These projects build on beginner skills by introducing more complex logic, external tool integration (even if simulated), and more nuanced outputs. They provide a deeper understanding of **function calling** and multi-step agent reasoning.

  • Objective: Build an AI agent using Flowise that can perform simulated web-based research on a given lead or company (e.g., finding basic info, recent news) and then draft a personalized, context-aware outreach email.
  • Key Skills: Flowise agent design, advanced prompt engineering for information extraction and personalized text generation, simulating function calling for "web research" (to demonstrate external tool interaction), multi-step reasoning.
  • Use Case: Assists sales, marketing, and business development professionals in automating the initial stages of lead qualification and personalized outreach, significantly reducing manual effort and improving communication effectiveness.
  • Set up a Flowise agent that accepts a target lead's name or company as input.
  • Implement a "research" step (mock or simple external tool integration for demonstration) to gather key details.
  • Design prompts to extract specific, relevant information from the "researched" data (e.g., recent achievements, industry).
  • Create a prompt chain to draft a personalized email, dynamically incorporating the extracted details.
  • Build a user interface in Flowise to input leads and review generated emails.
  • Objective: Develop an AI agent using Flowise that can perform calculations and execute simple Python code snippets based on user queries, demonstrating the agent's ability to integrate and leverage external computational tools.
  • Key Skills: Flowise agent design, LLM function calling for tool use, integrating a calculator tool, implementing a Python code interpreter, handling tool outputs, managing agent memory for conversational context.
  • Use Case: Enhances the agent's problem-solving capabilities, allowing it to provide accurate numerical answers or execute custom logic, making it valuable for data analysis, quick calculations, or even validating simple code snippets.
  • Design an agent workflow in Flowise that can identify mathematical queries.
  • Integrate a calculator tool into the agent, enabling it to perform arithmetic operations.
  • Set up a Python interpreter tool that the agent can "call" to execute user-provided Python code.
  • Create prompts that guide the LLM to correctly identify when to use the calculator versus the Python interpreter.
  • Test scenarios where the agent needs to perform calculations or interpret code, returning the results to the data.

These projects require a deeper technical understanding, involve more complex setups, or move into code-centric development and advanced AI architectures. They offer a comprehensive understanding of building, deploying, and optimizing powerful **AI agents**.

  • Objective: Build your own robust, custom AI assistant in Python, similar to Microsoft Copilot. This project focuses on developing a desktop application capable of sophisticated language processing and incorporating "vision" capabilities (e.g., processing information from simulated screen content or images).
  • Key Skills: Python programming, Visual Studio Code (VS Code) & Git proficiency, direct LLM API integration (e.g., OpenAI, potentially local LLMs), basic vision model integration (conceptual or via simple image processing libraries), desktop application development principles, security guardrails, performance optimization, and API cost management.
  • Use Case: Provides a blueprint for developing personalized, intelligent desktop assistants that can automate complex tasks, understand visual context, and enhance productivity directly on a user's computer.
  • Set up your Python development environment with VS Code and manage your project with Git.
  • Walk through and understand the provided Python codebase for a Copilot-like assistant.
  • Implement core language understanding and response generation capabilities using LLM APIs.
  • Integrate a "vision" component (e.g., by feeding descriptions of desktop elements or extracted text from images to the LLM).
  • Develop user interface elements for interacting with the desktop assistant.
  • Implement safety and disclaimer functions, especially for sensitive tasks.
  • Consider security implications, manage API costs, and optimize for speed and hardware performance.
  • Objective: Develop an advanced Retrieval-Augmented Generation (RAG) chatbot using Flowise that leverages local, open-source LLMs (via Ollama) and a private document knowledge base, ensuring all data processing remains on your local machine for enhanced privacy.
  • Key Skills: Local LLM deployment (using Ollama), Flowise for visual RAG pipeline construction, vector databases (e.g., ChromaDB, FAISS) for local knowledge storage, prompt engineering for accurate retrieval and generation, understanding the benefits and challenges of open-source models.
  • Use Case: Provides a solution for organizations and individuals requiring strict data privacy for their AI chatbots, allowing them to interact with sensitive internal documents or proprietary knowledge without sending data to external cloud services.
  • Install and configure Ollama and download open-source LLMs like Llama 3.1 locally.
  • Prepare a private document dataset (e.g., PDFs, text files) for the knowledge base.
  • Use Flowise to build a RAG pipeline:
    • Load and chunk the private documents.
    • Generate embeddings locally and store them in a local vector database.
    • Connect the local Ollama-powered LLM to the vector store.
  • Develop a chatbot interface that answers user queries solely from the local, private document knowledge base.
  • Experiment with different open-source LLMs and optimize for performance.
  • Objective: Learn to externally host and deploy AI agents (built with Flowise or custom code) on cloud platforms like Render, making them accessible to clients or for personal use as standalone applications or integrated into websites.
  • Key Skills: Cloud hosting platforms (e.g., Render), deployment strategies for Flowise applications, integrating AI agents into websites (iFrames, API calls), enhancing user interface (UI) and user experience (UX) for deployed chatbots, basic branding and styling.
  • Use Case: Enables developers and entrepreneurs to productize and share their AI agent solutions, offering them as services or integrating them seamlessly into existing web properties for broader accessibility and impact.
  • Prepare a previously built Flowise agent for cloud deployment.
  • Set up an account and navigate a cloud hosting platform (e.g., Render, Netlify).
  • Deploy your Flowise-based chatbot to the cloud, making it publicly accessible.
  • Practice integrating the deployed AI agent into a simple HTML webpage using iframes or API calls.
  • Experiment with visually improving the chatbot's appearance on a website, including branding, styling, and integrating relevant links.
  • Understand basic considerations for external hosting, such as scalability and reliability.
  • Objective: Master the techniques for integrating standalone AI agents into existing websites, transforming them into visually appealing, interactive chatbots or embedding their functionalities as native application features.
  • Key Skills: HTML, CSS, JavaScript for web integration, API interaction for agent communication, UI/UX design principles for chatbots, branding consistency, generating leads through agent interaction, potential integration of audio models.
  • Use Case: Provides the expertise needed to transform raw AI agent functionality into polished, user-friendly web experiences, enhancing user engagement and enabling new interactive features directly on a website.
  • Take a deployed AI agent and integrate its chat interface into a new or existing webpage.
  • Customize the visual appearance of the embedded chatbot to match website branding and style.
  • Implement methods for the chatbot to generate leads or collect user information.
  • Explore integrating additional functions, such as audio input/output (conceptual or using third-party APIs).
  • Ensure the standalone app feels appealing and is visually cohesive with the website's design.
  • Discuss strategies for making the chatbot more interactive and user-friendly through design choices.

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FAQ

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AI Council offers you the latest, appropriate and most importantly the real-world projects throughout your training period. This makes student to gain industry level experience and converting the learning’s into solution to create the projects. Each Training Module is having Task or projects designed for the students so that you can evaluate your learning’s. You will be working on projects related to different industries such as marketing, e-commerce, automation, sales etc.
Yes, we do provide the job assistance so that a learner can apply for a job directly after the completion of the training. We have tied-ups with companies so when required we refers our students to those companies for interviews. Our team will help you to build a good resume and will trained you for your job interview.
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To be very professional and transparent No, we don’t guarantee the job. the job assistance will help to provide you an opportunity to grab a dream job. The selection totally depends upon the performance of the candidate in the interview and the demand of the recruiter.
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A Capstone project is an outcome of the culminating learning throughout the academic years. It is the final project that represents your knowledge, efforts in the field of educational learning. It can be chosen by the mentor or by the students to come with a solution.
Yes, for obtaining the certificate of diploma programmer you have to submit the capstone project.