Skill Development

Agentic AI

Build autonomous AI agents that reason, plan, and act.

Hands-On Practical TrainingCertificate on Completion
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Weekly Outline

W1

Introduction to Agentic AI, AI agents, agent architectures, LLMs, prompts, tools, and agent workflows.

W2

Agent reasoning, planning, memory, context management, task decomposition, and decision-making.

W3

Tool calling, function calling, APIs, external tools, and connecting agents with real-world services.

W4

Agent memory, retrieval-augmented generation (RAG), vector databases, embeddings, and knowledge retrieval.

W5

Multi-agent systems, agent collaboration, communication, task delegation, and agent orchestration.

W6

Agent frameworks, workflow automation, autonomous task execution, and building practical AI agents.

W7

Agent evaluation, reliability, security, hallucination handling, monitoring, and responsible AI.

W8

Capstone project covering agent design, LLM integration, tools, memory, RAG, workflows, and deployment.

Weekly Assessment

An assessment is conducted at the end of every week to evaluate concepts and practical skills covered during that week.

Final Assessment

A comprehensive Agentic AI project covering AI agent development, tool integration, memory, RAG, multi-agent workflows, and deployment.

Skills Developed

Agentic AI fundamentalsAI agent architecture and workflowsLLM and prompt integrationAgent reasoning and task planningTask decomposition and decision-makingTool and function callingAPI and external tool integrationAgent memory and context managementRAG and knowledge retrievalEmbeddings and vector databasesMulti-agent systemsAgent orchestration and collaborationWorkflow automationAutonomous task executionAgent evaluation and monitoringAI agent security and reliabilityAgent deployment

Learning Outcomes

  • Understand the fundamentals and architecture of AI agents.
  • Design AI agents for specific tasks and workflows.
  • Integrate LLMs, prompts, tools, and external APIs into agents.
  • Build agents with reasoning, planning, memory, and context management.
  • Implement RAG using embeddings and vector databases.
  • Develop and coordinate multi-agent systems.
  • Automate workflows and autonomous tasks using AI agents.
  • Evaluate agent performance, reliability, and outputs.
  • Apply security and responsible AI practices.
  • Build, test, and deploy practical Agentic AI applications.