Gen AI can handle more than just text. Multimodal tools can understand and extract insights from multiple data types, like images, video, audio, and more. These capabilities help engineers build AI agents fine-tuned to perform a string of tasks, while LLM-powered agents can enable back offices to run 24/7.
For example, ChatGPT's voice feature eliminates the need for tedious typing. Users simply voice their instructions while the LLM application fetches a summary, creates charts, plans the fiscal year, and more. And with gen AI handling these time-consuming tasks, people can take on more strategic and innovative work.
You can see similar examples in the travel industry, where multimodal agents actively function as personal trip planners. The user only has to say "plan my holiday" to an app and give the LLM-powered agent access to their calendar. The agent will skim text, scan images, and generate a travel itinerary. It's a massive time-saver and productivity boost for travel agents, while customers enjoy quick and highly personalized recommendations.
However, as AI applications become more sophisticated, monitoring AI agents is crucial. Using agent operations (AgentOps), with analytics and debugging capabilities, is vital to mitigate risks like errors and biases in planning, task decomposition, reflection, and execution processes. Human oversight is also necessary to address these issues promptly. Likewise, embedding responsible gen-AI principles throughout the process is critical.