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The following part of agentic AI could be analysis and monitoring, as enterprises wish to make the brokers they’re starting to deploy extra observable.
Whereas AI agent benchmarks will be deceptive, there’s a number of worth in seeing if the agent is working the best way they wish to. To this finish, firms are starting to supply platforms the place prospects can sandbox AI brokers or consider their efficiency.
Salesforce launched its agent analysis platform, Agentforce Testing Middle, in a restricted pilot Wednesday. Common availability is predicted in December. Testing Middle lets enterprises observe and prototype AI brokers to make sure they entry the workflows and information they want.
Testing Middle’s new capabilities embody AI-generated checks for Agentforce, Sandboxes for Agentforce and Information Cloud and monitoring and observability for Agentforce.
AI-generated checks enable firms to make use of AI fashions to generate “lots of of artificial interactions” to check if brokers find yourself in how typically they reply the best way firms need. Because the identify suggests, sandboxes provide an remoted setting to check brokers whereas mirroring an organization’s information to mirror higher how the agent will work for them. Monitoring and observability let enterprises carry an audit path to the sandbox when the brokers go into manufacturing.
Patrick Stokes, government vice chairman of product and industries advertising and marketing at Salesforce, instructed VentureBeat that the Testing Middle is a part of a brand new class of brokers the corporate calls Agent Lifecycle Administration.
“We’re positioning what we predict will likely be a giant new subcategory of brokers,” Stokes mentioned. “Once we say lifecycle, we imply the entire thing from genesis to improvement throughout deployment, after which iterations of your deployment as you go ahead.”
Stokes mentioned that proper now, the Testing Middle doesn’t have workflow-specific insights the place builders can see the particular selections in API, information or mannequin the brokers used. Nonetheless, Salesforce collects that type of information on its Einstein Belief Layer.
“What we’re doing is constructing developer instruments to show that metadata to our prospects in order that they’ll really use it to higher construct their brokers,” Stokes mentioned.
Salesforce is hanging its hat on AI brokers, focusing a number of its power on its agentic providing Agentforce. Salesforce prospects can use preset brokers or construct custom-made brokers on Agentforce to connect with their cases.
Evaluating brokers
AI brokers contact many factors in a corporation, and since good agentic ecosystems goal to automate a giant chunk of workflows, ensuring they work properly turns into important.
If an agent decides to faucet the mistaken API, it may spell catastrophe for a enterprise. AI brokers are stochastic in nature, just like the fashions that energy them, and think about every potential likelihood earlier than developing with an consequence. Stokes mentioned Salesforce checks brokers by barraging the agent with variations of the identical utterances or questions. Its responses are scored as move or fail, permitting the agent to study and evolve inside a secure setting that human builders can management.
Platforms that assist enterprises consider AI brokers are quick changing into a brand new sort of product providing. In June, buyer expertise AI firm Sierra launched an AI agent benchmark referred to as TAU-bench to take a look at the efficiency of conversational brokers. Automation firm UiPath launched its Agent Builder platform in October which additionally supplied a way to guage agent efficiency earlier than full deployment.
Testing AI purposes is nothing new. Aside from benchmarking mannequin performances, many AI mannequin repositories like AWS Bedrock and Microsoft Azure already let prospects take a look at out basis fashions in a managed setting to see which one works finest for his or her use circumstances.
