Agentic AI Training in USA

This course explores the principles, design, and applications of Agentic AI — AI systems that go beyond passive prediction to actively pursue goals, take actions, and interact with digital and physical environments. Participants will learn about multi-agent systems, planning, reasoning, autonomous decision-making, tool-use, and orchestration frameworks. The course emphasizes hands-on exposure to building AI agents using modern frameworks (LangChain, AutoGPT, CrewAI, etc.), integrating them into workflows, and considering ethical and governance implications.

Welcome to the official Agentic AI Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in USA. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic understanding of AI and machine learning concepts.

• Familiarity with Python programming.

• Knowledge of APIs and automation tools is recommended.

• Optional: Exposure to LLMs (Large Language Models) like GPT or Claude.

• Building autonomous AI agents with Python & LLM frameworks

• Task planning, memory, and reasoning for agents

• Multi-agent coordination & collaboration

• Tool usage (web scraping, APIs, databases, RPA integration)

• Agent-driven automation workflows

• Debugging and monitoring autonomous agents

• Responsible design of autonomous AI systems

By the end of this course, participants will be able to:

1. Explain the concept of Agentic AI and its distinction from traditional AI.

2. Design and implement autonomous agents that can plan, reason, and execute tasks.

3. Integrate agents with tools, APIs, and external environments.

4. Use orchestration frameworks (LangChain, AutoGPT, CrewAI, etc.) to build multi-agent systems.

5. Apply agent-based AI in business, research, and real-world problem solving.

6. Understand ethical, safety, and governance challenges of autonomous AI systems.