The rapid deployment of AI agents represents a significant shift in technology, as they take on more complex tasks than traditional AI chatbots. However, challenges persist, especially in cross-domain decision-making where tasks require context from multiple fields. Autonomous agents face difficulties in executing multi-step processes that demand real-time adaptations and understanding of varying terminologies across departments. Internal data silos complicate these issues, necessitating a unified approach to data access for AI agents to improve contextual understanding and intelligence in their analyses and decision-making.
Even the smartest new autonomous agents are hitting their limits when they're assigned actions or required to make decisions which cross domains.
AI agents are built for multi-step tasks, but they still find sequential task execution to be a challenge.
Different departments tend to speak different data languages, use different systems, and can apply the same word or terminology in very different ways.
What's needed is to unify input data, empower AI agents to understand the full import of all the data they draw on.
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