GoKawiilDespite the C-suite's enthusiasm over artificial intelligence agents that can plow through office tasks like never-sleeping interns, the underlying technology is still rickety and a potential cost-sucker.
That much was clear this week during two separate events held in Silicon Valley, during which executives and engineers discussed the current excitement and challenges involving AI agents.
Kevin McGrath, the CEO of the AI startup Meibel said during a session that "the biggest problem that we're working with in AI right now," involves the misguided idea that everything needs to be processed by a large language model, or LLM.
"Just give all of your tokens and all of your money to an AI Claw bot that will just waste millions and millions of tokens," McGrath said, before explaining how companies need to be more deliberate when deciding which tasks are best suited for AI agents.
Since the recent rise of OpenClaw, a so-called "harness" that lets developers use various AI models to create and manage fleets of digital assistants, the tech industry has been pushing AI agents as the next big thing.
Nvidia CEO Jensen Huang told CNBC's Jim Cramer in March that it "is definitely the next ChatGPT."
But on Wednesday at the Generative AI and Agentic AI Summit in San Jose, technical staff from companies like Google and its DeepMind AI unit, Amazon , Microsoft and Meta revealed that creating and operating AI agents is not an easy task.
One session led by Google software engineer Deep Shah focused on new techniques intended to help manage the operational costs of running tons of AI agents.
It costs money to run AI agents, and a poorly designed and maintained system to monitor those digital assistants and their actions could potentially end up burning cash instead of saving it.
"If you think of a machine learning system or any multi-agent system, there are multiple challenges you will find when you try to deploy that system at scale," Shah said. "The first one is the inference cost."