Agentic AI course in Chennai

Credo Systemz offers the best Agentic AI training in Chennai to gain the in-built skills of Agentic AI, AI agents and to build strategic decisions for business problems. We focus on gaining real-time practical experiences to develop AI-powered agents under the guidance of industry experts. Our Agentic AI course ensures live sessions to learn the key concepts, tools and techniques of AI agents. This Agentic AI training in Chennai provides advanced skills of Agentic AI with real-world projects and case studies.

Advance Your Agentic AI Certification with Flexible Learning & No Cost EMI Options

Online Training
Corporate Training
Class Room Training
1V1 / Personal Training

Training

Case Studies and Projects15+
Hours of Practical Training80+
Placement Assurance100%
Expert Support24/7
Support & AccessLifetime
CertificationYes
Skill LevelAll
LanguageEnglish / Tamil

Why Choose a Agentic AI course from Credo?

Our Agentic AI Training in Chennai is the world class AI learning platform to gain the knowledge and practical skills of Agentic AI with artificial intelligence and LLM capabilities under the guidance of AI certified trainers. This Agentic AI course covers the skills to build AI Agents that can plan, act and observe results. To achieve the skills of Agentic AI with certification, join Our Agentic Artificial intelligence Chennai and gain the industrial knowledge of Agentic AI.

Agentic AI Training Syllabus

Credo Systemz Agentic AI course provides a well-structured curriculum with real-time practicals from scratch. This Agentic AI covers the necessary modules to expertise in AI, LLM, AI frameworks and tools using live sessions.

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What is Agentic AI?
Role of LangGraph, AutoGen, CrewAI in the ecosystem
OpenAI vs Azure OpenAI vs AWS Bedrock
Introduction to foundational concepts: Agents, Tasks, Graphs

LangChain recap: Chains, Tools, Memory
LangGraph architecture and why it matters
Installation, environment setup, and first LangGraph DAG

Nodes, Edges, State Machines
Understanding transitions and handlers
Building a simple agentic task flow

Deep dive into LangGraph Python SDK
Defining nodes and reactive transitions
Testing individual components with unit test strategy

Multi-agent interaction via graph state
Introducing dynamic task allocation
Conditional logic and loops in graphs

Memory, buffers, and shared state
Prompt engineering for modular agents
Using LangChain tools inside LangGraph

AutoGen vs LangGraph
AutoGen architecture and agent design
Basic use-cases and sample projects

Defining roles and communication protocols
Tool integration in AutoGen
Building helper agents and supervisor agents

Orchestrating AutoGen inside LangGraph
Handling multi-turn conversations
Error handling and edge case design

Designing agents for invoice interpretation
Simulating document variations
Defining success metrics for extraction

Tools: Azure Cognitive Vision, AWS Textract, Tesseract
Building OCR extractor modules
Integration with LangGraph pipeline

Data validation agents
Retry and fallback agents
Visualizing graphs with tools like Graphviz

Docker basics, images, volumes, networks
Writing Dockerfiles for Python/Node.js agents
LangGraph inside Docker

Docker Compose YAML structure
Orchestrating multiple agents/services
Environment variables and secrets

Local dev + container testing
Bind mounts, logging, and debug modes
Container-to-container communication

Pods, Deployments, Services, ConfigMaps
K8s vs Docker Compose
Setup Minikube for local testing

Writing Kubernetes manifests
Helm vs Kubectl
Deploying a sample LangGraph pipeline

Scaling agents
Managing state in distributed environment
Logging and monitoring via Prometheus & Grafana

Resource Group, App Service, Container Registry
Deploying Docker container to Azure Web App
Azure OpenAI API authentication & quota handling

ECS + Fargate for LangGraph
Using Bedrock for model inference
Integration with CloudWatch, IAM, Textract

GitHub Actions basics
Docker build & push workflow
Kubernetes auto-deploy pipeline

Rate limiting & retries
API Gateway/Reverse Proxy integration
Secure key management

LangGraph/AutoGen internal logs
Using OpenTelemetry
Tracing long-running agent flows

Token usage analysis
Latency optimization
Cost-performance balance in cloud

Structured outputs with ReAct and CoT
Use of external toolkits (LlamaIndex, Vector DBs)
Model adaptation and few-shot strategies

Capturing feedback on agent outputs
Self-healing agents with AutoGen feedback loops
Dynamic policy adjustment

End-to-end pipeline testing
A/B test experiments
Integration with synthetic data generation

Simulating multilingual invoices
Table extraction logic
Structured JSON/Excel output via agents

Prompt templating with LangChain
Personality config for agents
Context vs history vs memory tradeoffs

Each participant/team presents their initial design
Review and feedback from mentors

Debugging session
Review state transitions
Test cases writing session

Pick Azure or AWS for deployment
Secure deployment best practices

Review CI/CD
Final testing of K8s deployment

Showcase final projects
Peer review

Continuation of presentations
Instructor evaluation

Program summary and learning’s
Certification distribution
Discussion on next-level topics (RAG, LLMOps, etc.)

Flexible Mode of Training and Payment

Agentic AI Training in Chennai

To develop and showcase the Agentic AI skills, Credo Systemz provides the dedicated Agentic AI certification training in Chennai. Our Trainers are renowned AI experts with a handful of industrial experience and teaching ability in the tech field. To achieve the Agentic AI practical skills, our Agentic AI training provides live hands-on practical sessions and Placement support to reach great heights in today's AI-driven world.

60-78%

60-78% Chances of immediate placements.
80-89%

80-89% increase in salary.
~90%

90% of job market is open

Testimonials

I joined the Agentic AI training course in Chennai to gain the skills to build AI agents using real time practicals. My trainer is a knowledgeable person who provides live training to develop knowledge of Agentic AI. Each module includes detailed explanation, industrial practices to understand and build AIagents . I recommend Credo Systemz Agentic AI training as I had the best learning experience./p>
Subha Lakshmi
Credo Systemz Agentic AI training offered professional learning courses to build the knowledge of AI, LLM, Agentic AI. All Thanks to my trainer for his expert-level guidance and support. The hands-on sessions were useful in dealing with real time challenges. This Agentic AI training helped to move forward in my career path.

Dharshini

Agentic AI Course FAQs

Agentic AI is an AI system that can take initiative, plan, make decisions, and perform actions autonomously without constant human intervention. It can

Plan toward a specific goal,
Act in external environments,
Observe the results of its actions,
Adjust its plan based on feedback.

Agentic AI is the future of automation. Learning it helps you stay ahead in AI careers, giving you skills to create smart applications that make real-world decisions.

Working professionals, and tech enthusiasts with a basic understanding of AI, Python, or data science can enroll. No advanced experience required.

Fundamentals of AI Agents
Autonomous Decision-Making
Planning & Reasoning Systems
Tools like LangChain, AutoGPT, and OpenAI Agents

You’ll build AI agents that can automate workflows — such as data research bots, content creation assistants, or business task automators.

You can become an AI Developer, Automation Engineer, AI Product Specialist, or Agentic AI Engineer in top tech companies.

It’s completely hands-on — with live projects, practical demos, and guided labs to help you master real-world AI automation skills.

Agentic AI is creating new job roles focused on building, training, and managing autonomous systems — making it one of the most in-demand tech skills for the coming decade.

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Training

Why Choose a Agentic AI course from Credo?

Flexible Mode of Training and Payment

Testimonials

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