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AI+ Agent™
The AI+ Agent Certification is a forward-looking program designed to equip learners with the knowledge and skills to build, deploy, and manage intelligent AI agents. It provides a strong foundation in how autonomous agents operate, including decision-making, reasoning, and task execution powered by Artificial Intelligence (AI).
The course explores key concepts such as Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and multi-agent systems. Learners gain hands-on understanding of how AI agents interact with data, tools, and APIs to perform complex tasks autonomously across different environments.
It also covers practical applications of AI agents in business automation, customer support, workflow optimization, and data analysis. Emphasis is placed on agent orchestration, memory systems, and real-time adaptability to ensure effective performance in dynamic scenarios.
In addition, the program addresses ethical AI use, security considerations, and responsible deployment of autonomous systems. Through practical exercises and project-based learning, participants develop the ability to design and implement AI agents that enhance productivity and decision-making.
By the end of the certification, learners will be able to create intelligent agent-based solutions that drive automation, efficiency, and innovation across industries.
AI+ Agent™ – Course Outline
Program Overview
The AI+ Agent™ certification is designed to equip professionals with the skills required to design, build, deploy, and manage intelligent AI agents capable of autonomous decision-making and workflow automation. The program focuses on combining Large Language Models (LLMs), tool integration, reasoning frameworks, and multi-agent systems to create real-world AI solutions.
Participants will gain practical experience in agent architecture, prompt engineering, API integration, deployment pipelines, monitoring systems, and ethical AI practices. The course is highly hands-on, enabling learners to build functional AI agents that can interact with tools, retrieve information, execute tasks, and collaborate in multi-agent environments.
Course Objectives
By the end of this course, participants will be able to:
• Understand core principles and types of AI agents
• Design intelligent agent architectures for real-world applications
• Apply prompt engineering and reasoning techniques for agent behavior
• Integrate external tools, APIs, and knowledge sources into agents
• Develop and test autonomous workflow automation systems
• Deploy scalable AI agents using modern infrastructure
• Monitor, evaluate, and optimize agent performance
• Apply ethical, secure, and responsible AI practices
Target Audience
• AI/ML engineers and developers
• Software engineers and system architects
• Data scientists and automation specialists
• Product managers working on AI-driven systems
• IT professionals exploring agentic AI systems
• Digital transformation and innovation teams
• Students and professionals entering AI engineering
Course Duration
• Instructor-led training: 16–24 Hours
• Self-paced learning: Flexible modular completion
Assessment & Certification
• Module-based quizzes and knowledge checks
• Practical coding and no-code exercises
• Case study analysis
• Final capstone project evaluation
Certification:
Participants will receive the AI+ Agent™ Certification from AI CERTs® upon successful completion.
Training Methodology
• Instructor-led sessions (virtual/classroom)
• Hands-on labs and live demonstrations
• Case study-based learning
• Project-based assessments
• Interactive group activities
Course Modules
Module 1: Introduction to AI Agents
• Definition and evolution of AI agents
• Core characteristics of intelligent agents
• Types of agents: reflex, goal-based, utility-based, learning agents
• Reasoning frameworks: ReAct, Chain-of-Thought, ReWOO
• Real-world applications across industries
Module 2: Agent Architecture & Design
• Principles of agent-oriented system design
• Modular architecture and scalability considerations
• Role of Large Language Models (LLMs) in agents
• Tool use and external knowledge integration
• Multi-agent collaboration systems
• Memory and context handling in agents
Module 3: Prompt Engineering & Reasoning Strategies
• Advanced prompt engineering for agents
• Instruction tuning and structured prompting
• Chain-of-thought reasoning implementation
• Task decomposition and planning strategies
• Controlling agent behavior and outputs
Module 4: Development Frameworks & Implementation
• Overview of AI agent frameworks (LangChain, LlamaIndex, etc.)
• Building single-agent systems
• Developing tool-augmented agents
• Testing, debugging, and validation methods
• Case study: HR onboarding automation agent
• Case study: AI customer support assistant
Module 5: Multi-Agent Systems
• Introduction to multi-agent systems (MAS)
• Communication between agents
• Role-based agent design (planner, executor, reviewer)
• Coordination and task delegation strategies
• Conflict resolution in agent workflows
• Real-world enterprise use cases
Module 6: Infrastructure & Deployment
• Cloud infrastructure for AI agents
• APIs, containers, and microservices architecture
• CI/CD pipelines for agent deployment
• Scaling AI agents in production environments
• Security architecture and access control
• Deployment best practices
Module 7: Monitoring, Optimization & Lifecycle Management
• Performance monitoring of AI agents
• Logging, tracing, and observability tools
• Feedback loops and continuous improvement
• Cost optimization strategies
• Maintenance and lifecycle governance
• Case study: marketing automation agent optimization
Module 8: Ethics, Security & Responsible AI
• Bias detection in agent decision-making
• Data privacy and regulatory compliance
• Secure tool and API integration
• Hallucination risks and mitigation strategies
• Responsible AI design principles
• Trustworthy agent systems
Capstone Project: AI Agent System Development
• Define real-world problem statement
• Design end-to-end AI agent architecture
• Integrate tools, APIs, and reasoning workflows
• Implement autonomous decision-making logic
• Deploy and test working AI agent system
• Final presentation and evaluation
