Building AI agents involves combining several large language models (LLMs) to understand user intent, plan, and decompose tasks. However, LLMs have limitations, including the tendency to produce false information and the inability to reason. These issues can be mitigated by providing the right context to the agent and converting relevant knowledge into vector databases. Antich concludes by advising businesses to consider which processes they could outsource to a team of AI agents and to provide them with relevant knowledge context using vector RAG.
Key takeaways:
- AI agents are more versatile and powerful than chatbots as they can perform tasks for you, not just converse with you. They can automate repetitive tasks, analyze data, and provide summaries of relevant information.
- AI agents are built using several large language models (LLMs) that understand user intent, plan, decompose tasks into smaller steps, and orchestrate other agents to execute a task. They also integrate with existing software infrastructure.
- Two major limitations of LLMs are that they can produce false information if they don't know the right answer and they lack the ability to reason. However, these issues can be mitigated with retrieval augmented generation (RAG).
- Businesses can benefit from AI agents by thinking about which processes or workflows could be outsourced to a team of AI agents. It's important to provide these agents with relevant knowledge context using vector RAG.