Mastering AI Agents: Enterprise-Grade Intelligent Automation

What you will learn:

Architect and develop both standalone and collaborative AI agent systems leveraging cutting-edge structural designs.
Construct intelligent AI agents endowed with sophisticated memory, strategic planning, advanced reasoning, and dynamic tool invocation functionalities.
Implement and operationalize retrieval-augmented, data-intensive, and multi-modal AI agents for practical industry applications.
Engineer, supervise, and refine intricate agent workflows to enhance efficiency, minimize expenditure, and ensure dependable operation.
Integrate robust security protocols, establish governance frameworks, and adhere to responsible AI guidelines for production deployments of agent systems.
Seamlessly embed intelligent AI agents into critical organizational operations across departments such as Human Resources, Financial Services, Information Technology, and core business processes.

Description

A quick note: This program integrates and utilizes artificial intelligence (AI) technologies.

Intelligent AI agents are revolutionizing software development, strategic decision-making, and operational workflows across every sector. This intensive program offers a complete, practical exploration into architecting, developing, and implementing intelligent AI agent solutions—spanning core principles to sophisticated, production-ready enterprise systems. Structured over 52 engaging weeks, participants advance sequentially through the entire agentic AI development lifecycle, emphasizing continuous, applied knowledge acquisition rather than a singular final project.

The curriculum commences with a deep dive into the fundamental tenets of AI agents, encompassing diverse agent architectures, the perception-action cycle, sophisticated reasoning mechanisms, strategic planning, and various memory systems. Initial segments concentrate on deciphering the pivotal role of large language models (LLMs) in contemporary agent frameworks, distinguishing prompt-based directives from traditional programmatic control, and how autonomous agents adeptly deconstruct intricate objectives into manageable, executable operations. Practical laboratory sessions guide you through constructing your inaugural agents, integrating persistent memory, activating external tool utilization, and deploying advanced, structured reasoning paradigms that significantly surpass basic conversational interfaces.

Subsequent sections advance into prominent agent development frameworks, complex orchestration strategies, and synergistic multi-agent collaboration. You will acquire expertise in facilitating inter-agent communication, effective task delegation, conflict resolution within agent networks, and configuring agents to function as cohesive, synchronized systems instead of disjointed entities. Experiential labs prioritize genuine implementation: crafting sequential and concurrent operational workflows, meticulously debugging agent malfunctions, assessing output quality, and fine-tuning for optimal latency and economic efficiency. This provides invaluable hands-on proficiency in engineering agents that exhibit high reliability, inherent explainability, and quantifiable performance metrics.

A significant emphasis of this program is dedicated to developing knowledge-centric and data-informed AI agents. Participants will construct sophisticated retrieval-augmented generation (RAG) agents, seamlessly integrate diverse structured and unstructured data repositories, process various document types, and devise enduring memory architectures capable of long-term persistence and adaptive evolution. Furthermore, the curriculum delves into cutting-edge functionalities, including multi-modal agents adept at interpreting and reasoning across text, visual content, and auditory data, alongside real-time and event-responsive agents designed to react dynamically to fluid environmental inputs.

The instructional journey culminates by transitioning into designing and managing enterprise-grade AI agent platforms. You will learn to fortify agent security, mitigate prompt injection vulnerabilities, establish robust governance frameworks, and engineer efficient human-in-the-loop (HITL) processes. Critical subjects like comprehensive observability, proactive monitoring, rigorous version control, scalable deployment strategies, and astute cost optimization are covered, equipping you for successful agent implementation in live production settings. Principles of ethical AI and responsible development are thoroughly integrated, guaranteeing agents are inherently secure, fully transparent, and consistently aligned with corporate directives and industry best practices.

During the concluding phase, you will strategically apply agentic AI solutions across diverse, authentic business verticals such as Human Resources, Financial Management, Sales Optimization, IT Operations, Regulatory Compliance, Advanced Research, and Personal Efficiency Enhancement. Every weekly module features a rich blend of theoretical topics, two immersive hands-on laboratory exercises, and a pertinent homework assignment, ensuring skills are progressively solidified through ongoing practical engagement rather than reliance on a singular concluding project.

Upon successful completion of this program, participants will possess the confidence and expertise to conceptualize, develop, implement, and govern advanced AI agents that function autonomously, interact cohesively, and generate substantial business impact. This empowers them with indispensable, future-proof competencies for navigating the dynamically evolving landscape of agentic artificial intelligence.

Curriculum

Week 1: Introduction to AI Agents

This introductory week establishes the foundational understanding of AI agents, differentiating them from traditional automation. It covers core concepts like 'What is an AI Agent?' and contrasts 'Reactive vs Deliberative Agents' to highlight distinct operational paradigms. Practical labs guide learners to 'Build a rule-based chatbot' and 'Convert a script into an agent loop,' providing immediate hands-on experience. The week culminates in a homework assignment to 'Identify 5 real-world agent use cases,' encouraging critical thinking and application.

Week 2: Agent Architectures

Delving into structural aspects, this section explores the 'Perception–Action Loop,' which forms the backbone of intelligent agent behavior and interaction with environments. It also differentiates between 'Single-Agent vs Multi-Agent Systems,' explaining the complexities and benefits of collaborative AI. 'Environment Modeling' is introduced to understand how agents internalize and react to their surroundings. Hands-on exercises include 'Implement a perception–action loop' and 'Simulate environment states,' preparing students to 'Diagram an agent architecture' for homework.

Week 3: LLM Fundamentals for Agents

This week focuses on the underlying large language models (LLMs) that power modern agents. Topics include understanding 'How LLMs reason,' the critical role of 'Tokens, context windows,' and the fundamental distinction between 'Prompt vs Programmatic Control' in directing agent behavior. Labs involve creating a 'Prompt-controlled agent' to understand direct LLM interaction and a 'Tool-calling agent' to explore augmented capabilities, followed by homework to 'Compare prompt-only vs tool-augmented agents' for various tasks.

Week 4: Prompt Engineering for Agents

Mastering effective communication with LLMs, this module covers structured prompting techniques using 'System, User, Tool prompts' to guide agent responses. It introduces advanced reasoning patterns like 'Chain-of-Thought vs ReAct' for more robust decision-making and addresses crucial 'Prompt injection risks' for security awareness. Practical sessions include building a 'ReAct-based agent' to implement advanced reasoning and conducting a 'Prompt attack simulation,' with homework to 'Harden a prompt against misuse' for resilience.

Week 5: Agent Memory Concepts

Essential for intelligent and adaptive behavior, this week explores various memory types: 'Short-term vs Long-term memory' and their applications, the pivotal role of 'Vector databases' in storing and retrieving contextual information, and 'Episodic memory' for recalling specific events. Hands-on labs focus on practical implementation, demonstrating how to 'Add vector memory to an agent' and providing a 'Session memory implementation,' concluding with homework to 'Design a memory strategy' for a given agent use case.

Week 6: Agent Planning & Reasoning

This module dissects how agents approach complex problems by clarifying the distinction between 'Planning vs reasoning.' It covers 'Task decomposition,' a strategy for breaking down large goals into smaller, manageable sub-tasks, and introduces 'Goal hierarchies' for structured objective management. Practical labs guide participants in building a 'Task planner agent' and implementing a 'Multi-step reasoning chain,' with homework to 'Break down a complex task into sub-tasks' and define their dependencies.

Week 7: Agent Framework Overview

Learners gain an overview of popular agent development frameworks. Lectures cover 'LangChain,' 'CrewAI,' and 'AutoGen,' providing a comparative understanding of their architectures, strengths, and use cases. Labs involve implementing the 'Same agent in 2 frameworks' to highlight their operational differences and conducting a 'Framework comparison test' for performance and ease of use, with a homework assignment to 'Choose framework + justification' for a specific project.

Week 8: Tool-Using Agents

This week focuses on extending agent capabilities through external tools, enabling interaction with the real world. Topics include integrating 'API tools' for dynamic data fetching and actions, utilizing 'File & data tools' for information processing, and leveraging 'Search tools' for knowledge retrieval. Practical labs involve creating an 'API-enabled agent' to interact with external services and a 'Spreadsheet-aware agent' for data manipulation, followed by homework to 'Build a tool catalog' for an enterprise agent.

Week 9: Function Calling & Skills

Delving deeper into agent interactivity and robustness, this section distinguishes 'Functions vs tools' in LLM contexts, introduces 'Skill abstraction' for creating reusable agent competencies, and covers robust 'Error handling' strategies. Labs include developing a 'Function-calling agent' to execute specific code snippets and implementing 'Retry & fallback logic' for fault tolerance, with homework to 'Create reusable agent skills' for a common business process.

Week 10: Agent Orchestration

This module addresses the complex coordination of multiple agents and intricate workflows. It covers 'Sequential workflows' for step-by-step execution, 'Parallel agents' for concurrent task processing, and 'Event-driven agents' that react to real-time triggers. Hands-on labs demonstrate how to build a 'Sequential workflow' for a business process and perform 'Parallel task execution' for efficiency, concluding with homework to 'Design orchestration flow' for a multi-agent system.

Deal Source: real.discount