Agentic AI Course in Visakhapatnam
Build intelligent systems that can observe, reason, use tools, remember context, collaborate with other agents, and complete real-world tasks from prompt to production.
Why Learn Agentic AI Now?
Move beyond text generation into task execution
Agentic AI focuses on systems that can take goals, make decisions, use tools, and complete multi-step work rather than only generating one-off responses.
Learn the complete stack behind modern AI agents
The program connects LLMs, prompt engineering, memory, retrieval, orchestration, APIs, and deployment into one coherent learning path for real implementation.
Build portfolio-ready systems with practical patterns
Students work through chatbots, automation agents, research assistants, and multi-agent workflows so the outcome is project confidence, not theory alone.
Agent Architecture Snapshot
User goals, events, tasks, and business context
Short-term state and long-term stored knowledge
Reasoning, decomposition, and prioritization
APIs, search, retrieval, execution, and response
Agentic AI Curriculum
The curriculum is structured from fundamentals to advanced workflow design so learners understand what agents are, how they think, and how to build them for real-world use.
Understand the introduction to Agentic AI and the difference between Traditional AI, Generative AI, and Agentic AI. Learn what makes an AI system agentic and why that shift matters in modern software.
Cover characteristics of AI agents such as autonomy, goal-driven behavior, and decision-making. Then map agent architecture across input, memory, planning, tools, and output, and understand the loop: Observe → Think → Act → Repeat.
Study the role of Large Language Models in agent systems and learn prompt engineering basics including system prompts, task framing, guardrails, structured outputs, and prompt iteration for better reliability.
Explore leading tools and frameworks including LangChain, LangGraph, AutoGen, and CrewAI, and learn how agents connect to tools, APIs, services, and business systems in practical applications.
Learn memory in AI agents with short-term and long-term strategies, Retrieval-Augmented Generation, and how vector databases ground responses by retrieving the most relevant knowledge at runtime.
Dive into planning and reasoning, chain-of-thought reasoning, decomposition, verification, and the ReAct pattern where reasoning and action are interleaved for more capable task execution.
Compare single-agent and multi-agent systems, study collaboration between agents, and practice task delegation, role assignment, shared context, and coordinated execution for larger problem spaces.
Build practical systems such as chatbots, automation agents, and research agents by combining prompts, tools, memory, reasoning, and retrieval into end-to-end working applications.
Design agentic workflows using state machines, workflow orchestration, branching logic, retries, and event-driven execution so systems can operate reliably across longer-running business processes.
Study real-world use cases of Agentic AI, deployment of AI agents, API integration patterns, and approaches for scaling AI systems with observability, reliability, and maintainable architecture in mind.
Tools & Frameworks You'll Master
After This Program, You Will:
Explain how agent loops, memory, planning, tools, and outputs fit together in an Agentic AI system
Create better prompt strategies for LLM-driven applications and agent behaviors
Build memory-aware, retrieval-enabled agents using RAG and vector databases
Apply planning, reasoning, chain-of-thought, and ReAct style workflows to complex tasks
Design both single-agent and multi-agent systems with collaboration and task delegation patterns
Deploy and scale AI systems with APIs, orchestration, event-driven execution, and production readiness
Where This Takes You
AI Agent Developer
Build goal-driven assistants and automation agents
LLM Application Engineer
Create grounded, tool-using AI experiences
AI Automation Engineer
Connect agents to APIs, systems, and business workflows
Applied AI Solutions Architect
Design scalable multi-agent and orchestration patterns
Your Journey — Week by Week
Weeks 1–4
Foundations, characteristics, architecture, and agent loop
Weeks 5–8
LLMs, prompt engineering, frameworks, and integrations
Weeks 9–12
Memory, RAG, vector databases, planning, and ReAct
Weeks 13–16
Multi-agent systems, workflow orchestration, and deployment
Capstone
Launch an end-to-end agentic AI solution
Ready to Master Agentic AI?
Next batch starts May 2026. Limited seats available. Book your free demo class and experience how we teach modern AI systems through practical implementation.