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xpander.ai’s Agent Graph System makes AI agents more reliable, gives them info step-by-step
It enables AI agents to achieve a 98% success rate in multi-step tasks, compared to just 24% for agents using traditional methods.
Credit: xpander.ai Function calling, the backbone of most AI agent workflows, enables models to interact with external systems to perform tasks such as fetching real-time data or executing actions. Ran Sheinberg, co-founder and chief product officer at xpander.ai, explained in an interview with VentureBeat: “With AGS, we ensure the agent only uses the relevant tools at each step and follows the correct schema, enforcing precision and efficiency.” “We aimed to create an accessible platform that allows anyone to build AI agents, experiment with the technology, and start automating repetitive tasks to focus on what truly matters,” said David Twizer, co-founder and CEO of xpander.ai, in the same interview.
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