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The following part of agentic AI may be analysis and monitoring, as enterprises need to make the brokers they’re starting to deploy extra observable.
Whereas AI agent benchmarks might be deceptive, there’s loads of worth in seeing if the agent is working the way in which they need 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 Heart, in a restricted pilot Wednesday. Normal availability is anticipated in December. Testing Heart lets enterprises observe and prototype AI brokers to make sure they entry the workflows and knowledge they want.
Testing Heart’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 “a whole bunch of artificial interactions” to check if brokers find yourself in how usually they reply the way in which firms need. Because the title suggests, sandboxes supply an remoted surroundings to check brokers whereas mirroring an organization’s knowledge to mirror higher how the agent will work for them. Monitoring and observability let enterprises deliver an audit path to the sandbox when the brokers go into manufacturing.
Patrick Stokes, govt vice chairman of product and industries advertising and marketing at Salesforce, advised VentureBeat that the Testing Heart is a part of a brand new class of brokers the corporate calls Agent Lifecycle Administration.
“We’re positioning what we predict will probably be an enormous new subcategory of brokers,” Stokes mentioned. “Once we say lifecycle, we imply the entire thing from genesis to growth right through deployment, after which iterations of your deployment as you go ahead.”
Stokes mentioned that proper now, the Testing Heart doesn’t have workflow-specific insights the place builders can see the precise decisions in API, knowledge or mannequin the brokers used. Nonetheless, Salesforce collects that type of knowledge 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 raised construct their brokers,” Stokes mentioned.
Salesforce is hanging its hat on AI brokers, focusing loads of its vitality on its agentic providing Agentforce. Salesforce prospects can use preset brokers or construct personalized brokers on Agentforce to hook up with their cases.
Evaluating brokers
AI brokers contact many factors in a corporation, and since good agentic ecosystems purpose to automate an enormous chunk of workflows, ensuring they work effectively turns into important.
If an agent decides to faucet the incorrect API, it may spell catastrophe for a enterprise. AI brokers are stochastic in nature, just like the fashions that energy them, and take into account every potential chance 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 protected surroundings that human builders can management.
Platforms that assist enterprises consider AI brokers are quick turning into a brand new sort of product providing. In June, buyer expertise AI firm Sierra launched an AI agent benchmark known 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. Apart 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 surroundings to see which one works finest for his or her use instances.
