Enhancing AI Agents with Agentic Capabilities Training Course
Agentic AI enhances traditional AI agents by enabling adaptive decision-making, autonomous goal-setting, and self-improving behavior.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and automation specialists who wish to integrate agentic capabilities into AI-powered applications.
By the end of this training, participants will be able to:
- Understand the principles of agentic AI and autonomous decision-making.
- Implement goal-driven AI agents with self-optimization techniques.
- Integrate multi-agent collaboration for complex problem-solving.
- Enhance AI-human interaction through adaptive user experiences.
- Deploy agentic AI models in real-world applications.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
Introduction to Agentic AI
- Defining agentic capabilities in AI
- Key differences between traditional and agentic AI agents
- Use cases of agentic AI in various industries
Developing Goal-Driven AI Agents
- Understanding autonomous goal-setting and prioritization
- Implementing reinforcement learning for self-improvement
- Fine-tuning AI agent behaviors based on feedback loops
Multi-Agent Collaboration and Coordination
- Building AI agents that collaborate and communicate
- Task delegation and role assignment in agentic systems
- Real-world examples of multi-agent teamwork
Adaptive AI-Human Interaction
- Personalizing AI responses based on user behavior
- Context-awareness and dynamic decision-making
- Designing UX for intelligent and responsive AI agents
Deploying Agentic AI in Applications
- Integrating agentic AI with APIs and third-party tools
- Ensuring scalability and efficiency in AI deployments
- Case studies on successful agentic AI implementations
Ethical Considerations and Challenges
- Balancing autonomy with control in AI agents
- Addressing AI biases and ethical concerns
- Regulatory frameworks for autonomous AI systems
Future Trends in Agentic AI
- Emerging advancements in AI autonomy
- Expanding agentic capabilities with new technologies
- Predictions for AI-driven automation and decision-making
Summary and Next Steps
Requirements
- Basic knowledge of AI agents and automation
- Experience with Python programming
- Understanding of API-based AI integrations
Audience
- AI developers enhancing autonomous systems
- Automation engineers optimizing AI-driven workflows
- UX designers improving human-agent interactions
14 Hours
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