Agentic AI Training
Systems that plan and act, not just chat.
Learn to design autonomous AI agents that break down goals, call tools, remember context, and coordinate with other agents — the skill set behind the next generation of AI-powered automation.
Move from single prompts to autonomous, goal-driven agents
A chatbot answers one message at a time. An agent breaks a goal into steps, decides which tools to use, keeps track of progress, and adapts when something goes wrong — that shift from reactive to autonomous is what this program is built around.
You'll learn the core patterns behind agentic systems: planning loops, function/tool calling, memory design, and multi-agent coordination — then apply them to real automation problems, with safety and evaluation built in from the start rather than bolted on.
What you will learn
The core building blocks behind reliable, autonomous AI agents.
Agent Planning Loops
Design agents that reason step-by-step and adapt their plan as new information arrives.
Tool & Function Calling
Give agents structured access to APIs, databases, and external tools safely.
Agent Memory Design
Implement short-term and long-term memory so agents retain useful context.
Multi-agent Orchestration
Coordinate multiple specialized agents working together toward a shared goal.
Safety & Guardrails
Constrain autonomous actions with guardrails and human-in-the-loop checkpoints.
Evaluating Agent Behavior
Test agents for reliability, cost, and failure modes before shipping them.
Technologies & tools covered
Training roadmap
From single-agent planning to coordinated, multi-agent systems in production.
01. Foundations of Agentic Systems
2 Weeks- Agent vs. chatbot
- Reasoning loops
- Planning strategies
- Basic memory
02. Tool Use & Function Calling
2 Weeks- Defining tools
- Function calling APIs
- Structured outputs
- Error recovery
03. Agent Memory & State
1 Week- Short vs. long-term memory
- Vector memory stores
- Context window management
04. Multi-agent Orchestration
2 Weeks- Agent roles
- Coordinator patterns
- Message passing
- Task delegation
05. Safety, Evaluation & Guardrails
2 Weeks- Guardrails for autonomy
- Evaluating behavior
- Human-in-the-loop design
06. Deployment & Capstone Project
1 Week- Deploying agent services
- Monitoring & tracing
- Final project demo
Real-world projects you'll build
Agent systems built around real automation and operations problems.
Autonomous Research Assistant
An agent that plans a research task, searches for information, and drafts a structured report.
Multi-agent Customer Ops Bot
Cooperating agents that triage, investigate, and resolve incoming customer requests.
Workflow Automation Agent
An agent that automates a multi-step internal business workflow end to end.
Internship & industrial exposure
Real Automation Briefs
Work on agent problems modeled after real business automation requests.
Mentor Reviews on Agent Design
Get feedback on planning logic and tool design from practicing AI engineers.
Safety-first Practices
Practice the guardrail and review habits real teams use before deploying autonomous agents.
Learning methodology
Build-measure-adjust Cycles
Every agent you build is tested against real tasks and refined based on results.
1:1 Mentorship
Weekly sessions with a mentor experienced in agent system design.
Applied Assessments
Evaluated on working agent behavior, not theory quizzes.
Eligibility
Basic Python Knowledge
Comfort reading and writing basic Python is expected.
Familiarity with LLM APIs Helpful
Prior exposure to LLM APIs or our Generative AI program helps, but isn't mandatory.
Any Educational Background
Open to developers, students, and career switchers curious about AI automation.
Consistent Time Commitment
8–10 hours a week for hands-on practice and project work.
Career opportunities
Agentic AI Engineer
Design and build autonomous agent systems for real products.
AI Automation Engineer
Automate business workflows using agent-based systems.
LLM Application Engineer
Build applications that combine LLMs with tool use and orchestration.
AI Systems Developer
Focus on the infrastructure and reliability of agentic applications.
Applied AI Researcher
Explore and evaluate new agent architectures and patterns.
Freelance AI Automation Consultant
Deliver custom agent-based automation for clients.
Why choose YashOrbit
Safety-conscious Curriculum
Guardrails and evaluation are taught alongside capability, not as an afterthought.
Placement Assistance
Resume reviews, mock interviews, and referrals to hiring partners.
Real Deployment Experience
Every agent project is deployed and demoed as a working system.
Frequently asked questions
Generative AI focuses on building applications around LLMs (RAG, prompting, fine-tuning). Agentic AI goes further — teaching agents to plan, use tools, and act autonomously toward a goal across multiple steps.
Ready to build agents that plan and act on their own?
Join the next batch and go from single prompts to deployed, autonomous multi-agent systems.