The article also highlights recent research in multi-agent AI, such as the development of AgentRec, a method for selecting the most suitable AI agent for a given task using sentence embeddings. This research aims to improve the efficiency and accuracy of agent selection in multi-agent systems. The article suggests that generative AI can be trained to better match prompts with appropriate AI agents, enhancing its ability to handle complex tasks. As the field continues to evolve, users are encouraged to practice and refine their prompt engineering skills to effectively leverage the capabilities of multi-agentic AI systems.
Key takeaways:
- Prompt engineering is crucial for effectively utilizing multi-agentic AI systems, which involve generative AI and large language models performing various tasks.
- There are two main approaches to composing prompts for multi-agent AI: the driver's seat approach, where the user specifies which AI agents to invoke, and the passenger's seat approach, where the generative AI decides which agents to use based on the task.
- Understanding the capabilities and overlaps of different AI agents is essential for selecting the right agents to achieve desired outcomes and avoid unnecessary costs.
- Research is ongoing to improve the selection of AI agents using methods like sentence embeddings, enhancing the ability of generative AI to choose the most appropriate agents for a given task.