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Proleed's Agentic AI Training Course empowers you to master intelligent, goal-driven AI systems and build autonomous agents that think, decide, and innovate.
Average Salary of AI & Machine Learning Developer
Future of AI & Machine Learning Career
United States
Lead AI Specialist
Canada
AI Developer
India
Artificial Intelligence Engineer
Austin
Machine Learning Engineer
United States
Senior AI Engineer
India
Machine Learning Specialist
I joined Proleed's Agentic AI Training Course to upgrade my AI development skills, and it turned out to be the best decision for my career. Their instructors explained complex topics like LangChain, tool integration and autonomous agent design so clearly. Now I confidently build agent based systems at work, and it even helped me in getting a promotion as an AI Solutions Engineer.
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As a data scientist, I wanted to explore how AI is evolving beyond prompts - Proleed's Agentic AI gave me exactly that. Their hands on sessions on LangChain and OpenAI tools were incredibly practical. I especially loved how each week built upon the last. Proleed's made Agentic AI easy to understand, and now I am applying these concepts in real world automation projects.
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I have a decent knowledge of AI & Machine Learning but I was always curious about how Agentic AI actually works. Proleed's Structured learning path, from fundamentals to multi-agents, helped me bridge that gap. Their mentorship and project based approach made learning truly engaging. Now, I feel ready to shift into an AI research role, all thanks to this course.
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What is Agentic AI?
Evolution from Prompt-based to Agent-based systems
Differences between AI Assistants vs. Agents vs. Autonomous Systems
Basic Architecture of Agentic AI: LLM + Memory + Tools + Planning
Characteristics of Agents: Autonomy, Goal-setting, Reasoning
Reactive vs. Proactive Agents
Key capabilities: Tool use, long-term memory, task decomposition
Comparison: AutoGPT, BabyAGI, LangChain Agents
Introduction to agentic reasoning (ReAct, MRKL, Plan-and-Execute)
Limitations, risks, and ethics of autonomous agents
Introduction to LangChain ecosystem
LangChain Components: Chains, Tools, Agents, Memory
LLM wrappers and prompt templates
LangChain Agents: Simple vs. Multi-step agents
Tool usage with Agents (Google Search, Python REPL, API calls)
Memory types: Buffer, Summary, VectorStore, ConversationSummaryMemory
Output Parsers and Intermediate Steps
Handling long conversations and context windows
Decision-making flows in LangChain
Using LangChain Expression Language (LCEL)
Introduction to OpenAI Function Calling (GPT-4 & Tools API)
Designing structured functions for tool usage
JSON schema, argument parsing, function routing
Comparison: LangChain Tools vs. OpenAI Tools vs. ReAct
Calling APIs like weather, calculator, search with LLM
Multi-function invocation
Tool selection strategies and error handling
Introduction to OpenAI Assistants API
Using tools within context and chaining multiple calls
Common tool-using LLM applications: Retrieval, Execution, Summarization
Overview of reasoning strategies in LLMs
ReAct (Reasoning + Acting) pattern
Plan-and-Execute architecture (LangChain, Meta's implementation)
Task decomposition with LLMs
Using LLMs for planning tasks (To-do lists, workflows, subtasks)
Handling intermediate outputs, tool feedback
Agent decision flow: IF-THEN conditions, tool selection, fallback logic
Integrating external APIs and knowledge bases
Multi-agent orchestration and communication
Limitations of LLM-based reasoning (hallucinations, context loss)
Types of memory in Agentic AI
LangChain Memory integration with Agents
Vector Stores: FAISS, Chroma, Pinecone
Creating and using custom knowledge bases
Retrieval-Augmented Generation (RAG) principles
Agent memory vs. episodic memory vs. semantic memory
Context window optimization (chunking, summarizing, pruning)
Embeddings and retrieval flow
Integration with PDFs, docs, and chat history
Managing memory across multi-step tasks
What are autonomous agents?
BabyAGI and AutoGPT architecture and workflows
Task creation, prioritization, and execution loop
Feedback-driven planning and goal refinement
Limitations of open-loop and infinite-loop agents
Multi-agent systems: collaboration and coordination
Agent-Swarm frameworks (CAMEL, CrewAI, OpenAgents)
Distributed reasoning across agents
Multi-role agents (Planner, Executor, Critic, Researcher)
Agent chaining vs. Agent collaboration
Agent lifecycle: from design to deployment
Streamlit + LangChain-based interfaces
Gradio, FastAPI, Flask for building frontends
Logging, monitoring, and debugging agents
Guardrails and safety in agent responses
Evaluation metrics for agentic systems: task completion, correctness
Structured logging: LangSmith, PromptLayer
Managing latency, retries, API rate limits
User feedback integration and continual improvement
Role of human-in-the-loop in monitoring agents
Use cases in domains: Personal assistants, customer support, research, coding, automation
Comparison of agent frameworks: LangChain vs. AutoGPT vs. CrewAI vs. OpenAgents
Open-source vs. closed-source agent platforms
Introduction to emerging agentic AI tools (Flowise, SuperAgent, MetaGPT)
Microsoft AutoGen, ChatDev: Role-based agent teams
AI-first automation tools (Zapier AI, Relevance AI, TaskWeaver)
Limitations and risks: autonomy, misuse, hallucinations
Ethical and regulatory concerns around autonomous agents
Trends in embodied AI and robotics agents
Future of Agentic AI and LLMs as reasoning machines
| Duration | |
|---|---|
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2 Months (8 Weeks) |
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| Schedule | |||
|---|---|---|---|
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Weekdays Training Monday to Friday |
Daily Session
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Weekend Training Saturday & Sunday |
Weekly Session
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| US dollar | USD | 450 | 650 |
|---|---|---|---|
| Canadian dollar | CAD | 620 | 820 |
| Australian dollar | AUD | 700 | 900 |
| Sterling pound | GBP | 360 | 560 |
| New Zealand dollar | NZD | 780 | 980 |
| Indian rupee | INR | 35,000 | 55000 |
Fee is inclusive of all applicable taxes and include examination and certification fee.
No other hidden charges of any type.
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Live training sessions where you can ask queries while the class is going on
Access learning material anywhere and anytime you need though out lifetime
Doubt clearance session after every class from the same trainer to resolve your doubts
Unmatched 10:1 Student : Trainer Ratio to ensure personalized attention to every student
Working on live projects to
enhance your practical skills and knowledge
Proleed's Agentic AI Training Course with live classes provides a complete journey into the evolving world of intelligent, goal-driven AI systems. Our advanced course is designed to develop a thorough understanding of how agents think, plan and act on their own. It covers everything from defining foundational concepts and rationale, to cutting-edge topics like LangChain, using tools built by OpenAI, agent memory, and collaborative agent work.
The learning and structure of the course provides students with the hands-on experience of building AI agents in real world planning and automation capabilities. This program is perfect for students wanting to build their technical understanding and learn current developments of the evolving autonomous AI, all while providing a pathway for the future intelligent systems.
AI is introducing some of the biggest innovations of the century. The future of this field is sure to grow. Companies and industries worldwide continue to connect with the power of Artificial Intelligence, increasing the demand for specialists in the area.
In light of the rapidly changing AI landscape today, to have credentials to fulfil the expectations of most employers, you cannot simply be skilled in one area of AI. It is essential to also develop profound knowledge in the four principal areas of AI: Agentic AI, Machine Learning, Deep Learning, and Generative AI in order to be well-prepared for the positions involved in the field. These four areas are the building blocks of many current AI systems, and the understanding of how they fit together is becoming an essential part of research as well as industry. As AI progresses, professionals who have a solid understanding of these core areas will do so in a productive manner while also adapting to the changing nature of the field.
Proleed offers the daily one hour doubt clearing session for providing the better learning environment to students.
Proleed has industry’s best placement preparation process
that enables our students to get their dream IT Job.
Our experts will help you to make professional resume for you.
We will help you to build / optimize your linkedIn profile.
We will provide you Interview Questions & Answers.
Our experts will take mock interviews to make you confident.
We will provider Guidelines to access placement Network.
Finally, You got your dream
IT job
Recognized Worldwide for IT Jobs
We provide certificate verification system to speedily verify our
student's certificates online.
We provide certificate verification system to speedily verify our
student's certificates online.
Prerequisites: AI & Machine Learning Course
Anyone who have proper knowledge of AI & Machine Learning can easily enroll in Proleed’s Agentic AI Training Course to uplift his skills in this domain. Prior knowledge of AI & Machine Learning is mandatory to enroll in our course.
Demanding curriculum designed by industry experts
3+ Live Projects that includes real-time use cases
Hands-on experience on advanced tools, languages & technologies
Live doubt clearance session after every lecture
Placement Preparation Process
Here, we accept payment through different modes like Cash, Master Card, Visa Card, and Net Banking.
Yes Of course, Proleed’s all training courses allow you to learn every concept in in-depth form. It includes all levels of training from basic to advance along with real life based live projects. You will also get valid certification after the successful completion of course, so there is a no requirement of doing any other certification course.
Proleed gives the additional advantage of placement preparation along with all courses. After the successful completion of the Agentic AI training course, students are ready for the technical round of an interview, but there are some more aspects that the students require for clearing the interview. Our specialized placement cell provides placement assistance to all candidates.
There is a three different ways by which the training is delivered to candidates;
No worries, if you missed a class you can cover that topic without any problem, because our all the live lectures are recorded also. So, you can get the recorded videos of those missed sessions.
Agentic AI is transforming the future of Artificial Intelligence-Moving beyond simple prompts to autonomous systems capable of reasoning, planning and executing tasks. If you want to build a solid foundation in this cutting edge field and advance your professional growth, gaining practical experience with agentic systems is essential.
Proleed's Agentic AI Training Course caters to both students and professionals who are eager to learn these transformative concepts with clarity and practical depth. Our course covers the architecture of Agentic AI, combining LLMs, memory, tools and planning, while introducing frameworks such as LangChain, AutoGPT and OpenAI Function Calling. Learners gain experience in reasoning strategies like ReAct and Plan and Execute, multi agent collaboration and retrieval augmented generation. Through instruction and mentoring by industry experts, theory is reinforced with practical projects, equipping you to design, build and evaluate intelligent agents that can act and adapt autonomously in real applications of the world.
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