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Benefits of AI agents
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Task automation
With the ongoing advancements in generative AI, there is a growing interest in workflow optimization using AI, or intelligent automation. AI agents are AI tools that can automate complex tasks that would otherwise require human resources. This translates to goals being reached inexpensively, rapidly and at scale. In turn, these advancements mean human agents do not need to provide direction to the AI assistant for creating and navigating its tasks.
Greater performance
Multi-agent frameworks tend to outperform singular agents.11 This is because the more plans of action are available to an agent, the more learning and reflection occur. An AI agent incorporating knowledge and feedback from other AI agents specializing in related areas can be useful for information synthesis. This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence.
Quality of responses
AI agents provide responses that are more comprehensive, accurate and personalized to the user than traditional AI models. This is extremely important to us as users since higher-quality responses typically yield a better customer experience. As previously described, this is made possible through exchanging information with other agents, using external tools and updating their memory stream. These behaviors emerge on their own and are not preprogrammed.
Risks and limitations
Multi-agent dependencies
Certain complex tasks require the knowledge of multiple AI agents. When implementing these multi-agent frameworks, there is a risk of malfunction. Multi-agent systems built on the same foundation models may experience shared pitfalls. Such weaknesses could cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks.13 This highlights the importance of data governance in building foundation models and thorough training and testing processes.
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