Building Agentic AI Systems: From Theory to Practice Training Course
Agentic AI systems are designed to make autonomous decisions, improve themselves, and collaborate with multiple agents.
This instructor-led, live training (available online or on-site) is tailored for intermediate-level AI engineers, ML researchers, and developers who aim to build and implement Agentic AI systems in real-world 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 autonomously.
- 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 lecture and discussion sessions.
- Numerous exercises and practical activities.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training 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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