Complete AI Аgent Course

Master the art of building intelligent AI agents using LLMs, memory, reasoning, tool calling, RAG, vector databases, LangChain, LangGraph, CrewAI, and multi-agent systems. Design, deploy, and scale autonomous AI workflows for real-world business and enterprise applications.

Self-Paced

80 hours

21 modules

Recommended Course Audience

AI Engineers Machine Learning Engineers Generative AI Developers LLM Application Developers Data Scientists Python Developers Software Engineers AI Solution Architects Automation Engineers Technology Professionals and AI Enthusiasts

Pre-Required Skills

Strong Python programming fundamentals
Understanding of LLMs and Prompt Engineering
Familiarity with APIs, RAG, and AI workflows beneficial
Basic knowledge of automation and orchestration concepts helpful

Course Highlights

21 modules

Master the complete lifecycle of building production-ready AI agents using LangChain, LangGraph, CrewAI, AutoGen, MCP, A2A, Agentic RAG, GraphRAG, and AgentOps. Learn reasoning, memory, orchestration, security, deployment, and enterprise architectures while developing autonomous multi-agent systems capable of solving complex real-world business, analytics, and automation challenges. .

Understand the evolution of agentic AI, agent architectures, lifecycle, memory, reasoning, planning, tool usage, RAG integration, single and multi-agent systems, and enterprise design patterns for intelligent autonomous applications.

Learn LangChain components, chains, tools, memory, agent executors, workflow orchestration, custom agent development, integrations, and best practices for building scalable and production-ready AI agent applications.

Master graph-based agent workflows using LangGraph, including state management, branching logic, decision-making, workflow persistence, human intervention, and complex multi-step autonomous agent execution systems.

Build collaborative AI teams using CrewAI by defining agent roles, task delegation, communication strategies, workflow management, coordination mechanisms, and enterprise-grade multi-agent business solutions.

Explore AutoGen for creating autonomous conversational agents capable of collaboration, negotiation, planning, dynamic execution, and complex problem-solving through coordinated multi-agent interactions and workflows.

Learn MCP architecture, context exchange standards, client-server communication, tool interoperability, enterprise integrations, and building agents capable of accessing diverse external systems through standardized protocols.

Understand agent communication frameworks, messaging protocols, distributed coordination, agent discovery, collaboration standards, communication patterns, and scalable architectures enabling autonomous agents to work together.

Design intelligent memory systems using episodic, semantic, and long-term memory techniques, enabling context retention, personalization, retrieval optimization, and continuous learning across agent interactions.

Develop advanced reasoning capabilities using planning frameworks, Tree of Thoughts, Graph of Thoughts, reflection mechanisms, self-correction strategies, and intelligent decision-making for autonomous agents.

Build advanced retrieval architectures using Agentic RAG, GraphRAG, hybrid search, multi-hop retrieval, knowledge grounding, and enterprise knowledge systems for highly accurate agent responses.

Learn knowledge graph design, graph databases, semantic relationships, entity linking, reasoning mechanisms, and integrating structured knowledge systems into intelligent enterprise AI agents.

Explore orchestration strategies for managing multiple agents, workflow coordination, swarm intelligence, distributed execution, resource management, and enterprise-scale autonomous agent ecosystems.

Implement AgentOps practices including monitoring, tracing, logging, performance measurement, evaluation metrics, debugging, reliability engineering, and optimization for production AI agent systems.

Understand agent security risks, prompt injection prevention, access control, governance frameworks, responsible AI practices, compliance requirements, and guardrail implementation for safe deployments.

Design collaborative systems where humans supervise, approve, guide, and enhance autonomous agents through feedback loops, escalation workflows, governance processes, and risk mitigation strategies.

Learn deployment architectures, cloud infrastructure, APIs, containerization, scalability techniques, monitoring solutions, cost optimization, and best practices for enterprise-grade agent deployments.

Build AI-powered automation solutions for business operations, document processing, workflow optimization, enterprise tasks, operational efficiency improvements, and intelligent decision-driven automation systems.

Develop analytics agents capable of data interpretation, automated reporting, business intelligence generation, decision support, insights discovery, and intelligent analytical workflow automation.

Examine proven enterprise agent architectures, scalability strategies, reliability patterns, implementation frameworks, real-world use cases, and industry success stories across diverse domains.

Apply acquired skills to design, build, deploy, evaluate, and present enterprise-grade AI agent solutions integrating reasoning, memory, orchestration, automation, security, and real-world business requirements.

$299.00

This course is coming soon. Enrollment is not yet open.

Level : Intermediate

Modules : 21

Duration : 80 hours