- New Announcement !!! Upcoming batch from 05th March 2026
The Agentic AI Course focuses on building intelligent AI agents that can autonomously plan, reason, take actions, and interact with tools, systems, and users to achieve specific goals. Unlike traditional AI models that only respond to prompts, Agentic AI systems are capable of decision-making, task execution, self-improvement, and multi-step problem solving.
10:00 am – 7:00 pm Weekdays
10:00 am – 7:00 pm Weekends
Fully Interactive & Classroom Training
65,000 Students Enrolled
The course covers agent architecture, memory, planning, tool usage, orchestration, and safety considerations. By the end of the program, learners will be able to design, deploy, and manage intelligent Agentic AI systems for real-world applications.
Hi I am Kushal Dwivedi and I am super excited that you are reading this.
Our Agentic AI Course Trainers are industry experts with strong hands-on experience in Artificial Intelligence, Machine Learning, Generative AI, and Autonomous AI Systems. They bring real-world exposure from enterprise projects, startups, and advanced AI research, ensuring learners gain both theoretical knowledge and practical implementation skills.
Trainer Expertise
• What is Agentic AI
• Gen AI vs Agentic AI
• Autonomous vs assisted intelligence
• Real-world agent examples (Auto GPT, Baby AGI, Crew AI)
• Agent architecture overview
• LLM as the brain
• Memory types: short-term, long-term
• Tools and actions
• Environment and feedback loops
• Task decomposition
• Goal setting and prioritization
• Planning vs execution
• Reflection and self-correction
• ReAct framework (Reason + Act)
• Conversation memory
• Vector databases and embeddings
• Long-term memory design
• Knowledge retrieval strategies
• Memory optimization
• Why agents need tools
• Function calling basics
• API calling through agents
• Web search, file access, calculators
• Error handling and retries
• Single agent vs multi-agent architecture
• Role-based agents
• Collaboration and delegation
• Conflict resolution between agents
• Examples: Manager–Worker agents
• LangChain agents
• LlamaIndex agents
• CrewAI
• AutoGen
• Comparison of frameworks
• Guardrails and constraints
• Preventing infinite loops
• Monitoring agent actions
• Human approval checkpoints
• Ethical considerations in autonomous systems
• AI customer support agent
• Research and analysis agent
• Sales and outreach automation
• DevOps and coding agents
• Personal productivity agents
• Agent hosting options
• Cost optimization
• Logging and observability
• Performance evaluation
• Maintenance strategies
• Build an autonomous AI agent
• Examples:
o Research agent
o Task automation agent
o Multi-agent workflow system
• Live demo and review
Core Agentic AI Concepts: Understanding autonomous agents, task planning, memory, and decision-making
Building AI Agents: Designing and deploying agents that interact with tools, APIs, and data sources
Workflow Automation: Creating AI agents to automate business processes, reporting, and operations
Multi-Agent Systems: Coordinating multiple AI agents to solve complex tasks collaboratively
AI + Data Integration: Combining Agentic AI with Python, SQL, Power BI, and analytics workflows
Real-World Use Cases: Implementing agents for customer support, analytics, marketing, and operations
Production-Ready Skills: Deploying scalable, secure, and maintainable Agentic AI solutions
This hands-on, implementation-driven course ensures learners gain practical expertise in building intelligent, autonomous AI systems that deliver measurable business value.
Agentic AI refers to AI systems that can autonomously plan, make decisions, take actions, and interact with tools or environments to achieve specific goals with minimal human intervention.
Generative AI focuses on creating content (text, images, code), while Agentic AI goes a step further by enabling AI to reason, plan multi-step tasks, use tools, and act autonomously.
Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support. We Provide one of the best online data science course.
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
you will have access to all the recordings, you can go through the recording as many times as you want.
During the training and after as well we will be on the same slack channel, where trainer and admin team will share study material, data, project, assignment.
Data analytics is the process of analyzing, interpreting, and gaining insights from data. It involves the use of statistical and computational methods to discover patterns, trends, and relationships in data sets.
Data analytics involves a variety of techniques, such as data mining, machine learning, and data visualization. Data mining is the process of discovering patterns and relationships in large data sets, while machine learning is a type of artificial intelligence that enables computer systems to learn from data and improve their performance over time. Data visualization is the process of presenting data in a visual format, such as charts and graphs, to help people understand complex data sets.
The goal of data analytics is to turn data into insights that can be used to make informed decisions. This can involve identifying opportunities for business growth, improving operational efficiency, or predicting future trends and outcomes. Data analytics is used in many industries, including finance, healthcare, marketing, and government, to name a few.
In summary, data analytics is the process of analyzing data to gain insights and make informed decisions. It involves a range of techniques and tools to extract valuable information from data sets.
There are many companies that offer internships in data analytics. Some of the well-known companies that provide internships in data analytics are:
Google: Google offers data analytics internships where you get to work on real-world data analysis projects and gain hands-on experience.
Microsoft: Microsoft provides internships in data analytics where you can learn about big data and machine learning.
Amazon: Amazon offers data analytics internships where you can learn how to analyze large datasets and use data to make business decisions.
IBM: IBM provides internships in data analytics where you can work on real-world projects and learn about data visualization, machine learning, and predictive modeling.
Deloitte: Deloitte offers internships in data analytics where you can gain experience in areas such as data analytics strategy, data governance, and data management.
PwC: PwC provides internships in data analytics where you can learn how to analyze data to identify trends, insights, and opportunities.
Accenture: Accenture offers internships in data analytics where you can work on projects related to data analytics, data management, and data visualization.
Facebook: Facebook provides internships in data analytics where you can gain experience in areas such as data modeling, data visualization, and data analysis.
These are just a few examples of companies that provide internships in data analytics. You can also search for internships in data analytics on job boards, company websites, and LinkedIn.
Agentic AI commonly uses Large Language Models (LLMs), APIs, databases, automation tools, and agent frameworks to perform tasks and workflows.
you can write your questions at info@palin.co.in we will address your questions there.
Lets us know your experience, it will help others.