Building Agentic AI Systems: From Theory to Practice Training Course
Agentic AI systems are designed to make autonomous decisions, enhance themselves, and collaborate with multiple agents.
This instructor-led, live training (available online or on-site) is tailored for intermediate-level AI engineers, machine learning researchers, and developers who aim to construct and deploy Agentic AI systems in practical applications.
By the end of this training, participants will be able to:
- Grasp the fundamental principles of Agentic AI systems.
- Develop AI agents that can reason and act independently.
- Integrate Agentic AI with APIs and third-party services.
- Optimize interactions among multiple agents for complex tasks.
- Tackle ethical, security, and scalability issues in Agentic AI.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- For customized training options for this course, please contact us to arrange.
Course Outline
Introduction to Agentic AI Systems
- Defining Agentic AI and its capabilities
- Key differences between rule-based AI and autonomous AI
- Use cases and industry applications
Architecting Agentic AI Systems
- Frameworks and tools for building autonomous AI
- Designing AI agents with goal-driven capabilities
- Implementing memory, context-awareness, and adaptability
Developing AI Agents with Python and APIs
- Building AI agents
- Integrating AI models with external data sources
- Handling API responses and improving agent interactions
Optimizing Multi-Agent Collaboration
- Designing AI agents for cooperative and competitive tasks
- Managing agent communication and task delegation
- Scaling multi-agent systems for real-world applications
Enhancing Decision-Making in Agentic AI
- Reinforcement learning and self-improving AI agents
- Planning, reasoning, and long-term goal execution
- Balancing automation with human oversight
Security, Ethics, and Compliance in Agentic AI
- Addressing biases and ensuring responsible AI deployment
- Security measures for AI-driven decision-making
- Regulatory considerations for autonomous AI systems
Future Trends in Agentic AI
- Advancements in AI autonomy and self-learning systems
- Expanding AI agent capabilities with multimodal learning
- Preparing for the next generation of autonomous AI
Summary and Next Steps
Requirements
- Basic understanding of AI and machine learning concepts
- Experience with Python programming
- Familiarity with API-based AI model integration
Audience
- AI engineers developing autonomous AI systems
- ML researchers exploring multi-agent AI frameworks
- Developers implementing AI-powered automation
Open Training Courses require 5+ participants.
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Testimonials (2)
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
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