Agentic AI Course
Learn how to build autonomous AI agents using LangGraph, CrewAI, AutoGen, and more. This Agentic AI course offers hands-on experience with multi-agent systems, PromptOps, and RAG pipelines—tailored for professionals looking to master AI agent development.
Explore our flexible online Agentic AI program in India, with a detailed syllabus, certification, and placement support. Check course fees, apply for a free session, or download the full syllabus today.
Why Choose Our Agentic AI Course?
Build Real Autonomous Agents
Design and deploy agents with reasoning, memory, and multi-step workflows across real-world tasks.
Multi-Agent System Expertise
Coordinate teams of agents using CrewAI and LangGraph to solve complex enterprise problems.
Hands-on with LangChain & AutoGen
Master the most advanced frameworks to build, debug, and optimize agent-based applications.
Enterprise-Ready RAG & Memory
Fuse retrieval with long-term memory for smarter agent cognition using vector DBs and LangChain.
PromptOps & Agent Reasoning
Implement advanced prompting patterns—ReAct, CoT, ToT—for higher-quality agent decisions.
Secure Agent Deployment (MCP)
Deploy agents using Model Context Protocol (MCP), sandboxing, and secure function calling.
AgentOps, Tracing & Guardrails
Learn how to monitor, trace, and apply cost-control guardrails to production agent workflows.
SDKs for Real Deployment
Access OpenAI SDK, Google A2A, CrewAI, and LangGraph in real projects and GitHub-ready stacks.
Mentorship from Agentic Engineers
Learn from engineers building agent-based systems in real startups, products, and research labs.
Who Should Join This Agentic AI Course?
Designed for developers, engineers, and product leaders ready to build and deploy autonomous AI agents—master multi-agent orchestration, PromptOps, RAG pipelines, and production-grade deployment.
Top Skills You’ll Gain in Agentic AI Course
Agentic AI Tools & Frameworks You’ll Master
CrewAI
Role-Based Agent Collaboration
Coordinate role-specific agents with task delegation and parallel workflows.
AutoGen
Conversational Multi-Agent Framework
Enables multi-agent LLM communication with support for multi-turn dialogue and tool usage.
OpenAI Function Calling
Structured Tool Use
Allow LLMs to call APIs or tools using defined schemas to solve specific tasks autonomously.
Model Context Protocol (MCP)
Secure Tool Access
Enable secure, auditable, and sandboxed LLM-to-tool communication via WebSocket or SSE.
LangSmith
Agent Tracing & Evaluation
Debug, trace, and evaluate prompt chains and agent runs with visual insights.
PromptOps
Prompt Pattern Engineering
Operationalize ReAct, CoT, and other prompting strategies with version control.
RAG Flow
Retrieval-Augmented Agent Memory
Connect agents to knowledge bases via embeddings, vector search, and real-time recall.
LangGraph
Multi-Agent Workflow Orchestration
Graph-based orchestration framework for building reactive and persistent agents.
CrewAI
Role-Based Agent Collaboration
Coordinate role-specific agents with task delegation and parallel workflows.
AutoGen
Conversational Multi-Agent Framework
Enables multi-agent LLM communication with support for multi-turn dialogue and tool usage.
OpenAI Function Calling
Structured Tool Use
Allow LLMs to call APIs or tools using defined schemas to solve specific tasks autonomously.
Model Context Protocol (MCP)
Secure Tool Access
Enable secure, auditable, and sandboxed LLM-to-tool communication via WebSocket or SSE.
LangSmith
Agent Tracing & Evaluation
Debug, trace, and evaluate prompt chains and agent runs with visual insights.
PromptOps
Prompt Pattern Engineering
Operationalize ReAct, CoT, and other prompting strategies with version control.
RAG Flow
Retrieval-Augmented Agent Memory
Connect agents to knowledge bases via embeddings, vector search, and real-time recall.
LangGraph
Multi-Agent Workflow Orchestration
Graph-based orchestration framework for building reactive and persistent agents.
Course Roadmap – From Fundamentals to Real-World AI Agents
Agentic AI Foundations
Start thinking like an AI agent architect: • Agent types: Reactive, Goal, Utility, Learning • Core loop: Sense → Think → Act → Learn • Build your first agent using LangChain • Tools: LangChain, Python, OpenAI Functions
Build Agents with AutoGen
Framework for goal-driven multi-agent systems: • Roles, chats, tool routing in AutoGen • Tool chaining and data pipelines • Build: Email/report writing agent • Tools: AutoGen, OpenAI, Tavily
LangGraph Agent Workflows
Build DAG-based intelligent agent flows: • Nodes, edges, retry/fallback patterns • Memory + condition-based routing • Project: Customer support bot • Tools: LangGraph, LangChain Expression Language
PromptOps & Reasoning Patterns
Advanced prompting for tool-use agents: • CoT, ReAct, Tree of Thought, DSP • Prompt tuning for structured tools • Function-calling APIs and refinement • Tools: LangChain, OpenAI, PromptLayer
Model Context Protocol (MCP)
Secure tool calling across agent systems: • MCP client-server setup • Protocols: SSE, WebSocket • Use case: Agent-to-API bridge • Tools: MCP, LLaMA, OpenAI, SuperAgent
Retrieval-Augmented Agents
Bring context into agents with RAG: • Chunking, embedding, hybrid retrieval • Smart prompt merging with memory • Build: PDF chat + assistant agent • Tools: LlamaIndex, LangChain, FAISS
Multi-Agent Collaboration
Coordinate distributed intelligent agents: • Role assignment and task splits • Chat-based vs orchestration-based agents • Lab: Transcribe → Fetch → Visualize • Tools: AutoGen, CrewAI, LangGraph
Multimodal Agent Architectures
Integrate image, voice, and text intelligence: • CrewAI for text teams • Vision agents: CLIP, LLaVA • Voice agents: Whisper, XTTS • Tools: CrewAI, Hugging Face, LLaVA
Agent SDKs & Frameworks
Explore agent-native SDKs: • OpenAI Agents SDK: Tooling + Handoffs • Google A2A for decentralized agents • Compare: LangGraph vs AutoGen • Tools: OpenAI, Google A2A, LangGraph
AgentOps & Monitoring
Production-grade agent observability: • LangSmith, SuperAgent, Helicone • Cost tracing, error recovery • Guardrails, fallback strategies • Tools: LangSmith, Prometheus, Grafana
Agent Design Patterns
Architect scalable agent systems: • Centralized vs distributed flows • Stateless vs stateful memory patterns • CoT, ReAct, Event-driven agents • Patterns: DAG, Modular, Cooperative
Capstone Agent Projects
Demonstrate full-stack agent expertise: • Build: RAG-powered tutor agent • Debug assistant with memory • Multi-agent sales bot with vision • Stack: LangGraph, AutoGen, RAG
Agentic AI Foundations
Start thinking like an AI agent architect: • Agent types: Reactive, Goal, Utility, Learning • Core loop: Sense → Think → Act → Learn • Build your first agent using LangChain • Tools: LangChain, Python, OpenAI Functions
Build Agents with AutoGen
Framework for goal-driven multi-agent systems: • Roles, chats, tool routing in AutoGen • Tool chaining and data pipelines • Build: Email/report writing agent • Tools: AutoGen, OpenAI, Tavily
LangGraph Agent Workflows
Build DAG-based intelligent agent flows: • Nodes, edges, retry/fallback patterns • Memory + condition-based routing • Project: Customer support bot • Tools: LangGraph, LangChain Expression Language
PromptOps & Reasoning Patterns
Advanced prompting for tool-use agents: • CoT, ReAct, Tree of Thought, DSP • Prompt tuning for structured tools • Function-calling APIs and refinement • Tools: LangChain, OpenAI, PromptLayer
Multimodal Agent Architectures
Integrate image, voice, and text intelligence: • CrewAI for text teams • Vision agents: CLIP, LLaVA • Voice agents: Whisper, XTTS • Tools: CrewAI, Hugging Face, LLaVA
Multi-Agent Collaboration
Coordinate distributed intelligent agents: • Role assignment and task splits • Chat-based vs orchestration-based agents • Lab: Transcribe → Fetch → Visualize • Tools: AutoGen, CrewAI, LangGraph
Retrieval-Augmented Agents
Bring context into agents with RAG: • Chunking, embedding, hybrid retrieval • Smart prompt merging with memory • Build: PDF chat + assistant agent • Tools: LlamaIndex, LangChain, FAISS
Model Context Protocol (MCP)
Secure tool calling across agent systems: • MCP client-server setup • Protocols: SSE, WebSocket • Use case: Agent-to-API bridge • Tools: MCP, LLaMA, OpenAI, SuperAgent
Agent SDKs & Frameworks
Explore agent-native SDKs: • OpenAI Agents SDK: Tooling + Handoffs • Google A2A for decentralized agents • Compare: LangGraph vs AutoGen • Tools: OpenAI, Google A2A, LangGraph
AgentOps & Monitoring
Production-grade agent observability: • LangSmith, SuperAgent, Helicone • Cost tracing, error recovery • Guardrails, fallback strategies • Tools: LangSmith, Prometheus, Grafana
Agent Design Patterns
Architect scalable agent systems: • Centralized vs distributed flows • Stateless vs stateful memory patterns • CoT, ReAct, Event-driven agents • Patterns: DAG, Modular, Cooperative
Capstone Agent Projects
Demonstrate full-stack agent expertise: • Build: RAG-powered tutor agent • Debug assistant with memory • Multi-agent sales bot with vision • Stack: LangGraph, AutoGen, RAG
Industry-Trusted Agentic AI Certificate
Industry-Trusted Agentic AI Certificate
After completing this Agentic AI Course, you’ll earn a globally recognized certificate— proof you can design, orchestrate, and deploy autonomous AI agents at scale. Whether you’re upskilling or transitioning, this certificate validates your mastery of multi-agent systems, PromptOps, RAG, and production deployments.
Agentic AI Course vs Free Courses & Tutorials
| Feature | Agentic AI Course | Other Courses |
|---|---|---|
| Multi-Agent Architectures | ✔ Learn LangGraph, AutoGen, and CrewAI to build scalable multi-agent workflows with context sharing | ✘ Focuses on prompt chaining only; lacks orchestration |
| PromptOps & Agent Reasoning | ✔ Implements advanced PromptOps like CoT, ReAct, ToT, and ReWOO for step-by-step agent reasoning | ✘ Teaches only static prompts; no agent-level context |
| Secure Deployment (MCP) | ✔ Covers MCP for secure LLM-to-tool communication, sandboxing, and authenticated execution | ✘ No focus on secure agent invocation or context safety |
| RAG with Memory & Retrieval Agents | ✔ Hands-on RAG agents with long-term memory, vector search, and context chunking | ✘ RAG concepts are abstract; no persistent context |
| Mentorship & Project Feedback | ✔ Live mentor feedback on agent blueprints, prompt chains, and custom tool integrations | ✘ No live support or technical review on agent designs |
| Capstone Certification & Portfolio | ✔ Portfolio-ready capstone projects with versioned code, tool usage, and evaluation reports | ✘ Basic certificate only; no verified outcomes |
| Placement Support & ROI | ✔ One-time ₹40,000 with job prep, referral network, and career support until placement | ✘ No placement pipeline or structured outcome tracking |
Agentic AI Course Fees
Included Benefits:
- Mentorship from Agentic AI engineers.
- Capstone agent projects + deployment guidance.
- Placement assistance: mock interviews, resume help, referrals.
- Lifetime access to recordings & future updates.
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