Building AI Agents and Agentic Workflows Specialization course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to AI Agents and Agentic Systems

Estimated time: 12 hours

• Define AI agents and their role in automation
• Compare AI agents with traditional AI systems
• Explore agent autonomy, planning, and decision-making
• Examine real-world applications of AI agents

Module 2: Designing Agent Architectures

Estimated time: 16 hours

• Understand agent memory and state management
• Design single-agent workflows using LLMs
• Implement reasoning and action loops
• Orchestrate agent-environment interactions

Module 3: Multi-Agent Systems and Tool Integration

Estimated time: 16 hours

• Design collaborative multi-agent frameworks
• Integrate APIs and external tools into agents
• Connect agents with databases and services
• Build modular and scalable agent systems

Module 4: Implementing Agentic Workflows with LLMs

Estimated time: 14 hours

• Use LLMs for task planning and execution
• Chain agent actions into coherent workflows
• Incorporate feedback and adaptation loops
• Optimize workflow efficiency and reliability

Module 5: Deployment, Safety, and Monitoring

Estimated time: 12 hours

• Deploy agents in production environments
• Monitor agent performance and behavior
• Apply safety guardrails and responsible AI practices

Module 6: Final Project

Estimated time: 20 hours

• Design an autonomous AI agent system
• Integrate tools, memory, and multi-agent collaboration
• Submit a deployable agent workflow with documentation

Prerequisites

• Familiarity with Python programming
• Basic understanding of large language models (LLMs)
• Foundational knowledge of AI and machine learning concepts

What You'll Be Able to Do After

• Design and implement intelligent AI agents
• Build task-oriented agents using LLMs
• Construct agentic workflows for automation
• Integrate tools, APIs, and memory into agent systems
• Deploy and monitor agent-based applications responsibly