LLM-Based AI Agents for Enterprise Automation

• Strong understanding of AI and machine learning
• Experience with Python programming
• Familiarity with large language models (LLMs) and natural language processing (NLP)

Audience

• AI engineers
• Enterprise software developers
• Business leaders

With the rise of open-source models like DeepSeek, Mistral, and LLaMA, enterprises are deploying custom AI agents for various workflows.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, enterprise software developers, and business leaders who wish to customize and deploy LLM-based AI agents for enterprise applications.

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

• Understand the architecture and capabilities of open-source LLMs.
• Customize and fine-tune LLMs for enterprise use cases.
• Deploy AI agents using LangChain and Hugging Face.
• Integrate LLM-powered agents into business workflows.

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.

Introduction to Open-Source LLMs

• Overview of DeepSeek, Mistral, LLaMA, and other open-source models
• How LLMs work: Transformers, self-attention, and training
• Comparing open-source LLMs vs. proprietary models

Fine-Tuning and Customizing LLMs

• Data preparation for fine-tuning
• Training and optimizing LLMs using Hugging Face
• Evaluating model performance and bias mitigation

Building AI Agents with LLMs

• Introduction to LangChain for AI agent development
• Designing agent-based workflows with LLMs
• Memory, retrieval-augmented generation (RAG), and action execution

Deploying LLM-Based AI Agents

• Containerizing AI agents with Docker
• Integrating LLMs into enterprise applications
• Scaling AI agents with cloud services and APIs

Security and Compliance in Enterprise AI

• Ethical considerations and regulatory compliance
• Mitigating risks in AI-driven automation
• Monitoring and auditing AI agent behavior

Case Studies and Real-World Applications

• LLM-powered virtual assistants
• AI-driven document automation
• Custom AI agents for enterprise analytics

Optimizing and Maintaining LLM-Based Agents

• Continuous model improvement and updating
• Deploying monitoring and feedback loops
• Strategies for cost optimization and performance tuning

Summary and Next Steps