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Our Consulting approach to the adoption of AI and intelligent automation is human-centered, pragmatic, outcomes-focused and ethical.
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Agents overseeing agents
In their purest form, agents are nothing new. The simplest example may be the humble thermostat—select the desired temperature, and the system will regulate itself without further prompting. The same principle holds true for smart thermostats and smart grids alike—set a goal, and agents will sense and self-correct until they’ve achieved it.
Perhaps the most heralded agents of the past decade or so are autonomous vehicles, which combine sensing, navigation, and driving systems to reach their destinations. Their long and arduous rollout also underscores the limitations of agents trained on prior machine learning techniques requiring them to learn every given situation. New and established autonomous vehicles firms are now pivoting to generative models with a higher degree of autonomy.
While agents based on LLMs are still prone to hallucinate, one solution is to tightly scope what’s asked of them. Another approach, as seen in the Smallville experiment, is to assign one agent to assess the work of another, which, at scale, can mitigate the effects of a few rogue actors.
This ability to interact and learn from each other has enormous implications for business. While some firms are already building digital avatars and home healthcare agents, the CEO of a leading video conferencing app predicts the solution to calendars packed with meeting invites will be sending agents in our stead. But why stop there? In a world accustomed to remote and hybrid work, specialized agents trained on the firm’s proprietary data will simply become coworkers. “Just as you’d create a legal- or technical-diligence team to assist in evaluations, now agents will be part of that team,” Malhotra says.