Build AI that doesn’t just answer, it gets work done
Our Agentic AI course in Hyderabad teaches you to build agents that plan, use tools, remember context and work together to finish real tasks.
Ask SMALL i about this course →# goal: resolve a customer ticket
plan = agent.plan(goal)
for step in plan:
result = tools.run(step)
agent.reflect(result)
return agent.report()
What is agentic AI?
An agent is an AI system that can plan, call tools, remember what it did and keep going until a goal is reached.
Break big goals into steps and adapt when something fails.
Let agents use APIs, databases, browsers and code.
Keep short-term context and long-term knowledge.
Coordinate several agents with clear roles and handoffs.
A chatbot replies. An agent researches, writes, checks and updates systems. Teams are now looking for people who can build agents that are useful and safe.
The loop every agent follows
Understand this loop and every framework becomes easier.
Read the goal and the current context.
Decide the steps and which tools to use.
Call tools and gather results.
Check the result and fix mistakes.
Return the outcome or hand off to a human.
11 modules in our Agentic AI course in Hyderabad
Open any module to see what you will learn. Modules marked Hands on include practical lab work.
01Introduction to Agentic AI
- From LLMs to Agents, Agent Types and Use Cases
- Agent Components: Planning, Memory, Tools, Reflection
02LLM and Prompt FoundationsHands on
- LLM APIs, Function Calling, Structured Outputs
- Prompt Patterns for Agents: ReAct, Plan-and-Execute
03Tool Use and Function CallingHands on
- Designing Tools and Schemas
- API, Database, Browser, and Code Execution Tools
- Model Context Protocol (MCP) Servers and Clients
04Agent FrameworksHands on
- LangChain and LangGraph: Graphs, State, Nodes, Edges
- CrewAI, AutoGen, LlamaIndex Agents
- OpenAI Agents SDK and Claude Agent SDK Overview
05Memory and Knowledge
- Short-Term and Long-Term Memory
- RAG for Agents, Vector Stores, Knowledge Graphs
- Context Management Strategies
06Multi-Agent SystemsHands on
- Orchestrator and Worker Patterns, Role-Based Agents
- Agent Communication, Handoffs, Delegation
- Human-in-the-Loop Workflows
07Planning, Reasoning and Reflection
- Task Decomposition, Self-Critique, Reflection Loops
- Reasoning Models and Test-Time Compute
08Evaluation and Observability
- Agent Evaluation Metrics, Benchmarks, Test Datasets
- Tracing and Logging with LangSmith and Similar Tools
- Cost and Latency Optimization
09Safety, Security and Governance
- Prompt Injection, Tool Permissions, Sandboxing
- Guardrails, Auditability, Responsible Deployment
10Deployment
- FastAPI Services, Docker, Cloud Hosting
- Workflow Automation with n8n and Integrations
11Capstone Projects
- Research Agent, Coding Agent, Customer Support Agent, Business Workflow Agent
Tools you will use in our Agentic AI course in Hyderabad
The tools used in real projects and listed in most job descriptions.
Why learn agentic AI
Skills that stay useful as tools and trends change.
Agents automate multi-step work, not just conversations.
Prompting, APIs, memory, evaluation and deployment come together.
Automate research, support and internal processes.
Learn permissions, sandboxing and human-in-the-loop design.
Tracing and evaluation help agents get better over time.
Few people can build agents well today.
Roles you can aim for
Titles vary by company, but these are common starting points and next steps.
Designs and builds agents for business tasks.
Python · LangGraphConnects agents to tools and workflows.
n8n · APIsBuilds reliable applications around language models.
LangChain · FastAPIDesigns teams of agents with clear roles.
CrewAI · AutoGenTests, traces and improves agent behaviour.
LangSmith · EvaluationHelps businesses pick and launch agent use cases.
Agents · CommunicationWhat you will be able to do
A balance of technical ability and the habits employers look for.
- Tool use: design tool schemas and function calling.
- Frameworks: LangGraph, CrewAI, AutoGen and agent SDKs.
- Memory and RAG: give agents knowledge and context.
- Deployment: FastAPI, Docker and workflow automation.
- Planning: break goals into reliable steps.
- Evaluation: test agents with datasets and metrics.
- Safety thinking: handle prompt injection and permissions.
- Cost sense: balance quality, speed and spend.
Build a portfolio you can show
Capstone work and interview preparation are part of the course.
Search, read and summarise a topic for you.
Plan, write and test small programs.
Resolve tickets using tools and a knowledge base.
Automate a multi-step process end to end.
Show your agents with clear demos.
Practise agent design questions.
Who this Agentic AI course in Hyderabad is for
You do not need to be an expert to begin.
Programmers moving into AI applications.
People who know prompts and want to build more.
Professionals who automate business processes.
Founders and managers exploring AI agents.
Agentic AI course in Hyderabad: your questions
Not here? Ask us on WhatsApp or talk to SMALL i.
Do I need coding experience?
Basic Python helps. The course starts with LLM and prompt foundations before moving to agents.
How is this different from generative AI?
Generative AI focuses on models that create content. Agentic AI uses those models to plan and take actions with tools. We recommend the Generative AI course first if you are brand new.
Are agents safe to run on company systems?
They can be, with the right controls. The course covers permissions, sandboxing, guardrails and human approval steps.
Do I need to know generative AI first?
Not strictly, because the first modules cover the LLM basics you need.
Will I get a job after the course?
No institute can promise a job. Working agents in your portfolio are the strongest proof of skill.
Ready to start learning Agentic AI?
Meet our trainer in a free live demo, or ask SMALL i your questions first.