At AGAI, we harness the transformative potential of Generative AI across multiple domains. Our work is grounded in rigorous methodologies and spans a diverse set of themes, including:

• Retrieval-Augmented Generation (RAG): In our research, we study the impact of various indexing and retrieval algorithms on the energy consumption in RAG systems. Our goal is to democratize access to advanced capabilities using low-resource usage.
• Mitigating Hallucinations and Bias: We research techniques and algorithms to reduce errors and biases in AI systems.
• Agentic AI Security: Our group researches the implementation of LLM security measures to address several issues including prompt injection, jailbreaking, and model poisoning.
• Multi-agent System Security: AGAI researches the multi-agent security, reliability and scalability using Zero-trust Authentication mechanisms for multi-agent, collaborative systems. 
  
  Through these explorations, we aim to shape a future where Generative AI empowers transformative innovations, responsibly impacting society and industry alike.