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1. Intro to Agentic AI Ramp up - for ai beginners and mid

Intro to Agentic AI Ramp up.pptx

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Intro to Agentic AI Ramp up
Purpose and Scope What's Up in the world of AI agents ? What will we focus on during this course?
>>> Overwhelming Flash News >>> ➢ “Only 5% of AI agents monetize” ➢ “Whoever tells you that OpenAI's 𝐀𝐠𝐞𝐧𝐭 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 released yesterday killed Zapier, n8n, Make and co has not touched any of these platforms.” Link 🡪 ➢ Agentic Design Patterns ➢ 82% accuracy looks great in a benchmark. In real life, Google warns, it still ends in failure if you chase demos over discipline. Link 🡪 ➢ 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗖𝘆𝗯𝗲𝗿 𝗕𝗿𝗲𝗮𝗰𝗵 𝗪𝗼𝗻'𝘁 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 . 𝗖𝗼𝗱𝗲 𝗜𝘁 𝗪𝗶𝗹𝗹 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻
Power and the money, money and the power Minute after minute, hour after hour Everybody's runnin', but half of them ain't lookin' It's going on in the kitchen, but I don't know what's cookin’ Coolio - Gangsta's Paradise (feat. L.V September 2025
…and I pray, oh my god do I pray, I pray every single day For a revolution. 4 Non Blondes - What's Up (Offic ial Music Video) >>> Tech Evolution >>>
Further Reading Link
Pay the toll to the angels Drawing circles in the clouds Lady Gaga - Abracadabra ( Official Music Video) LLM (Large Language Model): The core engine trained on massive text data to understand and generate human-like language. It powers most modern AI agents. AI Agent: A system that uses reasoning, planning, and decision-making to perform tasks autonomously. It often wraps around an LLM to add goal-oriented behaviour. RAG (Retrieval-Augmented Generation): Enhances LLMs by retrieving relevant external documents at runtime to improve accuracy and context. Ideal for dynamic or domain-specific queries. Fine-Tuning: A method to adapt a pre-trained LLM to specific tasks or domains by training it further on curated datasets. It improves performance but is resource-intensive. MCP (Model Context Protocol): A newer framework that allows models to access structured context (like PDFs or databases) in a standardized way, improving multi-step reasoning and document interaction. >>> Definitions >>>
LLM and more..
Model Evaluation Criteria ● Accuracy ● Precision ● Recall ● F1-Score ● AUC-ROC
Agentic AI Ramp-Up Program 1. Into and Teasers for Agentic AI Ramp Up 2. Prompting Techniques for Better LLM Control 3. RAG 4. Tools, and MCP for Enhanced Context 5. Building Autonomous AI Agents and Systems 6. Agent to Agent Communication and Collaboration 7. One Workshop to rule them all 8. Creating Effective Agents within ZI Environment
Multi Agents Don't push me too far, don't push me too far. Tonight, tonight. U2 - The Unforgettable Fire (Offi cial Music Video)
Topologies Source: agent-architectures Supervisor Network Custom
Building Blocks of Agents
in the context of AI, the technique called Retrieval-Augmented Generation , which enhances large language models (LLMs) by retrieving information from an external knowledge source to use in generating a response. "RAG" can refer to a torn piece of cloth or,
Types of RAG Type Description Use Case Simple RAG Retrieves once, then generates a response based on retrieved documents. FAQ bots, basic knowledge queries Multi-hop RAG Retrieves multiple documents across steps to answer complex questions. Research assistants, legal/medical AI Iterative RAG Refines retrieval and generation in cycles to improve accuracy. Scientific writing, summarization Hybrid RAG Combines dense and sparse retrieval methods for better coverage. Enterprise search, customer support Streaming RAG Continuously retrieves and updates context during long conversations. Chatbots, tutoring systems Contextual RAG Uses user history or session context to guide retrieval. Personalized assistants Chain-of-Thought RAG Retrieves reasoning steps or intermediate facts before generating final output. Math solvers, logic- based tasks Tool-augmented RAG Integrates external tools (e.g., calculators, APIs) during retrieval. Finance, engineering, data analysis Multi-modal RAG Retrieves across text, images, or audio sources. Visual Q&A, multimedia search Domain-specific RAG Tailored to niche datasets like legal, medical, or technical corpora. Expert systems Combines retrieval with autonomous agent Complex workflows,
Classic
Complex …
Who can tell the difference between… RAG (Retrieval-Augmented Generation) Chatbot ● Core Function: Combines a large language model (LLM) with an external knowledge base (documents, web) to provide accurate, source-cited answers. ● Process: Retrieves relevant data first, then generates a response based only on that retrieved context. ● Capability: Great for Q&A, internal knowledge bases, fact retrieval (e.g., "What's our PTO policy?"). ● Limitation: Cannot perform actions or plan beyond retrieving and generating text. Tool-Augmented Chatbot ● Core Function: Extends RAG by connecting the LLM to external tools, APIs, databases, or code execution. ● Process: Can use retrieved info and trigger external functions (e.g., search the web, run code, fetch live data) within a conversation. ● Capability: Performs specific actions like booking appointments, checking real-time stock prices, or running calculations. ● Limitation: Lacks complex, autonomous planning or self-correction; typically follows direct tool-use instructions. AI Agent ● Core Function: A higher-level system that reasons, plans, and acts autonomously to achieve complex goals. ● Process: Decomposes goals, selects and sequences tools (including RAG & other tools), executes steps, evaluates results, and self-corrects (learning loop). ● Capability: Orchestrates multi-step workflows, makes decisions, and learns from interactions (e.g., "Plan a business trip and book flights/hotels"). ● Key Difference: Proactive, goal-oriented, and autonomous, unlike the more reactive nature of RAG or tool-augmented systems.
Model Context Protocol (MCP)
MCP in the service of RAG or Agent
Choose RAG when your primary goal is to provide up-to-date, factual information to the LLM from a specific, dynamic data source like a company's knowledge base. Select Fine-tuning when you need to change the model's behavior, style, or reasoning to master a specialized domain like coding or legal terminology Prompt Engineering Vs RAG Vs Finetuning Explained Easily
Context Engineering Context engineering is the strategic design of data, workflows, and environments to provide AI models (especially LLMs) with the optimal, relevant information needed for accurate, reliable, and useful responses, going beyond simple prompting to manage memory, external knowledge, and user specifics. It involves curating conversation history, user profiles, documents, and real- time data to guide the AI's understanding and behavior, ensuring enterprise alignment and scalability.
Prompt Engineering Prompt engineering is the process of designing and refining inputs (prompts) to guide generative AI models, such as Large Language Models (LLMs), to produce accurate, relevant, and high-quality responses. It is a multidisciplinary field that combines linguistic skill, logic, and experimentation to "program" AI using natural language instead of traditional code. We'll always be together, Together in electric dreams… The Leaf People in lockdown do T ogether In Electric Dreams Giorgi o Moroder & Phil Oakey cover
LangChain Key Features • LangChain manages applications' interaction with different LLMs. • Chains sequence actions to achieve specific complex goals. • Retrieval (RAG) connects LLMs to external data sources for context. • Agents are reasoning engines that select and use various tools. • Memory allows applications to remember past conversation context.
LangGraph Key Features • Models applications as a state machine for complex workflows • Uses Nodes to represent specific tasks and functions • Edges define conditional paths and dynamic decision-making • The State object stores shared memory and centralizes information
LangChain LangGraph LangChain's features are in several modules: ● Model I/O: Manages how applications interact with different LLMs. ● Chains: Sequences of actions that connect multiple parts to achieve a specific goal. An example is summarizing a document and then translating it. ● Retrieval (RAG): Tools that connect LLMs to external data sources. (e.g. PDFs, databases, and web pages) to provide context-aware responses. ● Agents: Reasoning engines that use an LLM to decide which tools to use. (e.g. can be a calculator or web search). ● Memory: Tools that allow applications to "remember" previous parts of a conversation. This enables coherent multi-turn dialogue. LangGraph models applications as a state machine using three primary building blocks: ● Nodes: Functions that represent specific tasks, such as calling an LLM, searching a database, or performing a calculation. ● Edges: Define the paths between nodes. They can be conditional, allowing the system to decide the next step dynamically based on the current state. ● State: A centralized object (often a TypedDict) that stores the shared memory of the entire workflow. Every node can read from and update this state.
Study! (things are moving fast) • NVIDIA turned AI learning into a zero-c ost masterclass. Here are 8 legit course s with no free trial traps or paywall gim micks • Free ZI: • Google skills • Linkedin learning • Newsletters (choose only one) • https://www.foma.news/ • linkedin - #gen AI • Youtube • Langgraph Tutorial You can sleep forever, but still you will be tired You can stay as cold as stone, but still you won't find peace Faithless - One Step Too Far (Offici al Video) ft. Dido
Building the core of an agent - Technologies ● LangGraph ● LangSmith ● (Vertex AI) ● Agent-to-Agent (A2A) protocol ● OpenAI, Antropic and Microsoft agent builders
Why are we here?
Complex…
Katy Perry - Chained To The Rhythm (Official) ft. Skip Marley
Katy Perry - Chained To The Rhythm (Official) ft. Skip Marley
1. Intro to Agentic AI Ramp up - for ai beginners and mid