AI for Business: 4 Rules for Building Reliable AI Agents
AI agents are moving into real workflows more and more often, but trust in them depends not on the power of the model but on the process. Thomson Reuters advises measuring quality through benchmarks and experts, seating designers next to data scientists, and giving agents access not to everything, but only to already vetted corporate tools and external practices.
AI-processed from ZDNet AI; edited by Hamidun News
Thomson Reuters has shared recommendations for companies rolling out AI agents in their workflows: success depends not on the magic of the models, but on disciplined development, testing and integration with existing tools.
How can you verify the reliability of AI agents for business?
For agents, this is harder than for ordinary software. It is not enough to check that an answer looks right; you need to define the success criteria formally, in advance.
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