Build with code
Turn ideas into Python-based workflows with tools, services and data connections.
AGENTIC AI • HYDERABAD + ONLINE
Agentic AI Course in Hyderabad.
Go beyond chatbots. Learn to build AI agents that reason, use tools, retrieve knowledge and execute multi-step workflows with LangGraph, CrewAI, AutoGen and Python.
Practical agent workflowsCapstone project pathMentor-guided learning
Agent workflow · Execution● ACTIVE
GOALResearch and summarize an industry topic
01
Plan the task
✓Break down objectives and choose next steps
02
Use tools + knowledge
✓Retrieve context, call APIs, search data
03
Check and deliver
✓Validate result and prepare the output
> workflow.status: completed
> steps.executed: 3
> output: report_ready
> steps.executed: 3
> output: report_ready
Learn the modern AI stack
Understand the difference
From asking AI questions to building AI that acts.
Traditional LLM applications typically respond to a prompt. Agentic AI systems combine language models with tools, planning, context and control logic to carry out structured work over multiple steps.
At AI Fusion-X, the focus is on implementing these ideas in code, evaluating agent behaviour and understanding the limitations and safeguards needed before deployment.
- Agent reasoning loops
- Tool and API integration
- RAG and memory
- Multi-agent coordination
Why learn this
Skills that go beyond prompting.
Explore the engineering decisions behind agents: tool permissions, state, reliability, monitoring and practical use cases.
Reason and route
Work with stateful graphs, branching paths, retries and human approval steps.
Connect knowledge
Add retrieval, vector databases and context handling to grounded agent responses.
Evaluate and ship
Learn about testing, failure handling, observability and production considerations.
The learning path
A structured Agentic AI syllabus.
An illustrative eight-week path covering core agent concepts through a practical capstone. Actual batch duration and sequence may vary.
PHASE 01Weeks 1–2
Foundations & tools
Understand agent architectures and develop your first tool-using AI workflow.
- LLM foundations, prompts and structured outputs
- Agent loops, ReAct concepts and decision points
- Function calling and external API integration
- Basic Python-based agent implementation
Build: Basic tool-calling agent →
PHASE 02Weeks 3–4
Memory & planning
Add useful context, retrieval and multi-step task planning to agent systems.
- Short-term state and long-term knowledge
- Vector stores and semantic retrieval
- Retrieval-augmented generation (RAG)
- Task decomposition and execution planning
Build: Knowledge-aware assistant →
PHASE 03Weeks 5–6
LangGraph workflows
Move from linear chains to controlled, stateful agent orchestration.
- Nodes, edges and shared state
- Conditional routing, loops and checkpoints
- Retries, human-in-the-loop review
- Tool execution guardrails and evaluation
Build: Multi-step LangGraph workflow →
PHASE 04Weeks 7–8
Multi-agent & deployment
Combine specialised agents and prepare an application for demonstration.
- CrewAI and AutoGen concepts
- Agent roles, handoffs and orchestration
- FastAPI and basic cloud deployment patterns
- Testing, reliability and capstone presentation
Build: Capstone research agent →
Syllabus shown is based on the existing course outline. Confirm current batch dates, exact duration, prerequisites, included tools, fees and delivery mode with our team before enrolling.
Practice with purpose
Projects that demonstrate agent behaviour.
Examples of projects explored in an Agentic AI learning path; the exact assignments depend on the batch syllabus.
Research assistant
Collect information through tools, extract relevant findings and generate a structured report.
Tool callsPlanning
Knowledge-base agent
Answer questions against a document collection using retrieval and source context.
RAGVector DB
Customer-support agent
Route requests, consult approved information and escalate complex cases to a person.
WorkflowHuman review
Data analyst agent
Use a controlled Python workflow to analyse data and create readable summaries.
PythonData
CAPSTONE / 05
Multi-agent research workflow
Coordinate a planner, researcher and reviewer to deliver a traceable result.
LangGraphMulti-agentEvaluation
Technology stack
Frameworks & tools you'll explore.
Learn how each component fits into a real agent workflow, rather than memorising a list of tools.
LangGraph
Stateful, graph-based workflows.
CrewAI
Role-based team coordination.
AutoGen
Agent conversations and handoffs.
LangChain
Models, prompts, tools and retrieval.
Python
Core programming and integrations.
OpenAI API
Model calls and tool execution.
FastAPI
Expose agents through web endpoints.
ChromaDB
Vector-based knowledge retrieval.
Career-oriented learning
Make your skills easier to demonstrate.
Build a practical portfolio and understand what engineering teams expect from reliable AI agent systems.
Roles to explore
Agentic AI skills may be useful for roles such as these, depending on experience, fundamentals and hiring requirements.
AI EngineerLLM Application DeveloperAI Agent DeveloperAutomation EngineerAI Solutions Engineer
Career preparation
Learning support can help you present and discuss your practical engineering work.
- Project portfolio guidance
- Resume and profile improvement
- Technical interview preparation
- Discussion of realistic career pathways
Career outcomes and any placement assistance depend on individual skills and current opportunities; employment is not guaranteed.
Questions answered
Frequently asked questions.
Get a quick overview before booking a demo or discussing your learning path.
What is Agentic AI?
Agentic AI refers to systems that use AI models along with planning, tools, memory and control logic to carry out tasks involving multiple steps. Reliable systems also require testing, safeguards and appropriate human oversight.
Do I need prior Machine Learning knowledge?
Advanced Machine Learning is not necessarily required to start, but familiarity with basic Python and how APIs work is helpful. Contact the team for the prerequisites of your chosen batch.
What will I learn in this Agentic AI course?
The outline covers agent architectures, tool calling, planning, memory, RAG, LangGraph workflows, multi-agent concepts including CrewAI and AutoGen, and deployment basics.
What projects can I practise?
Example projects include a research assistant, document knowledge agent, support workflow, data analyst agent and a multi-agent capstone. Project assignments can vary with the batch.
How long is the course and when does the next batch start?
This page shows an illustrative eight-week plan based on the existing syllabus. Ask AI Fusion-X to confirm the active batch duration, dates, timings and available seats.
Is online or classroom learning available?
Contact AI Fusion-X to confirm current learning modes. Batch delivery, mentor support, recordings and classroom arrangements can vary by course and schedule.
What is the fee for the Agentic AI course?
Fees, payment options and inclusions should be confirmed with the admissions team. Book a free demo or contact AI Fusion-X for the current quotation.
Is placement support included?
Career guidance may include portfolio feedback, resume improvement and interview preparation. Specific support should be confirmed for your batch; a job offer or placement is not guaranteed.
Ready to build your first AI agent?
Discuss the syllabus, prerequisites, fees and next batch with AI Fusion-X.