AI agents and agentic AI Training With TechPratham, you can learn how to use AI agents and agentic AI to make systems that can think, plan, and act on their own. Find out how to make, use, and connect AI-powered agents for real-world uses.
Level
Advanced
Duration
8 weeks
This Techpratham course goes deep into AI Agents and Agentic AI, focusing on how self-driving systems talk to each other, think, and solve problems. You will learn about things like multi-agent systems, planning, making decisions, and how to connect with LLMs. You'll learn about agent frameworks, reinforcement learning, and real-world deployment scenarios through hands-on projects. By the end, you'll know how to make AI-powered agents for business, automation, and more complex AI workflows.
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Understand the fundamentals of intelligent agents and agentic workflows.
Learn the natural language processing fundamentals required for working with LLMs, including text representation, tokenization, and embeddings.
Explore the neural network concepts behind LLMs, focusing on the transformer model, attention mechanisms, and encoder-decoder structures.
Dive into the training process of LLMs, including data requirements, pretraining, fine-tuning, and optimization techniques.
Study the most widely used LLMs like GPT, BERT, and T5. Compare their structures, training objectives, and applications.
Learn how to craft effective prompts for LLMs to get desired outputs. Understand zero-shot, few-shot, and chain-of-thought prompting.
Customize LLMs for domain-specific use cases. Explore instruction tuning, LoRA, and reinforcement learning with human feedback (RLHF).
Learn techniques to evaluate the performance of LLMs using both quantitative metrics and qualitative assessments.
Understand how to deploy LLMs in production environments using APIs, cloud platforms, and optimization for cost-effective scaling.
Address challenges in LLMs such as bias, hallucinations, and misuse. Learn principles for building safe and ethical AI systems.
Create an AI agent that can search the web on its own, collect data, summarize what it finds, and give you useful information. This project mixes reasoning with retrieval-augmented generation.
Make a multi-turn conversational agent that uses LLMs to answer customer questions, deal with problems, and work with CRM systems. Pay attention to memory, personalization, and making choices.
Make an AI agent that takes care of boring business tasks like setting up meetings, writing reports, and answering emails. This project will test how well agents work together and how well they connect to external APIs.
Ai Agents and Agentic Ai – Associate Training Program
What is the difference between AI Agents and traditional AI models?
Traditional AI models are designed to do one thing at a time. AI Agents, on the other hand, are self-contained systems that can see, think, and act in real time. Agentic AI goes a step further by combining agents with reasoning, planning, and LLMs to deal with complicated workflows in the real world.
What are AI Agents?
AI Agents are autonomous programs that can perform tasks, make decisions, and interact with users or other systems. They operate using AI models and predefined rules. TechPratham’s training teaches learners to understand, design, and deploy AI agents for business and research applications.
What is Agentic AI?
Agentic AI refers to AI systems with decision-making autonomy, capable of initiating actions without constant human input. These agents can learn, adapt, and optimize tasks independently. TechPratham explains how Agentic AI is applied in automation, robotics, and intelligent systems.
How do AI Agents differ from traditional AI tools?
Unlike traditional AI that only responds to prompts, AI Agents can plan, act, and self-correct to achieve goals. They are proactive rather than reactive. TechPratham guides learners in designing and implementing intelligent agent-based solutions.
Can AI Agents be used in business?
Yes, AI Agents can automate customer service, data analysis, scheduling, and decision-making. They improve efficiency and reduce human workload. TechPratham provides real-world case studies and hands-on projects demonstrating business applications.
Do I need programming skills to work with AI Agents?
Basic programming knowledge is helpful but not mandatory for some platforms. TechPratham teaches both technical and non-technical learners how to use and customize AI Agents effectively.
What industries use AI Agents?
AI Agents are used in finance, healthcare, e-commerce, robotics, IT, and smart home systems. TechPratham aligns its projects with industry-specific scenarios to prepare learners for real-world implementation.
How do AI Agents make decisions?
They use algorithms, machine learning models, and reinforcement learning to analyze data and choose optimal actions. TechPratham trains learners on decision-making frameworks and evaluation of AI Agent performance.
Are AI Agents safe and reliable?
AI Agents can be safe if designed with ethical guidelines, monitoring, and fail-safes. TechPratham teaches best practices for responsible AI deployment to minimize risks and ensure reliability.
What skills can I gain from AI Agents training?
Learners acquire autonomous system design, reinforcement learning, decision-making, multi-agent collaboration, and AI workflow automation. TechPratham uses hands-on labs to ensure these skills are industry-ready.
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