- Google, Microsoft and OpenAI are all driving the shift from AI assistants to proactive AI agents
- These agents promise to bring more intuitive interactions with complex data systems
- But fully autonomous AI systems are still far away
Software publishers are exploring a new paradigm: agentic artificial intelligence (AI)and they use it for better data analysis and business intelligence. The broader shift to AI agents “signals a shift in the perception and maturity of AI,” said Alexander Wurm, principal analyst at Nucleus Research.
Unlike traditional AI assistants designed to follow commands, agentic AI systems are designed to proactively analyze, recommend, and even take action on users’ behalf. While organizations “still value AI assistants provided by technology vendors,” Wurm said they are increasingly experimenting with personalized use cases using API-enabled AI services.
Google Cloud, for example, is privately testing Looker, a conversational data analytics agent that connects to popular workplace tools like Slack, Microsoft Teams, and Google Chat so users can interact with data from the platforms they they already use to communicate.
Tableau, a subsidiary of Salesforce, was an early adopter of AI’s proactive capabilities, rebranding its AI assistant as an agent. Meanwhile, OpenAI announced plans to launch its own AI agent as a research and development tool in January, by Bloomberg. And smaller companies like ThoughtSpot are also entering the space with tools like Spotter, their agentic AI analyst.
According to Wurm, first-mover advantage will not define the market: “Instead, solutions that best connect underlying data with semantic contexts and business logic will see the greatest adoption.” In this context, he said, vendors like Google, Microsoft and Salesforce are “uniquely positioned” to succeed.
“Several other vendors offer agent products, but many are still in public or private preview,” Wurm told Fierce Network. Nucleus Research expects to see a proliferation of agent solutions from “the vast majority” of software vendors next year.
The Driving Forces of Agentic AI
At the heart of this transition is the need for more intuitive interactions with complex data systems.
“GenAI has captured the imagination of all the amazing applications that could be created,” Peter Bailis, vice president of engineering at Google Cloud, told Fierce.
Google Cloud’s Looker agent allows users to query and analyze data in natural language. Instead of needing to know complex query languages or navigate dashboards, users can ask questions like “What were the sales numbers last month?” » and get immediate information.
For users, the rise of AI agents shifts the focus from answering basic questions to driving strategy and results. “It’s exciting,” Bailis said. “Imagine asking complex, multi-step questions about private data and getting meaningful answers. This is the long-term unlock in the generative AI and data space.
Bailis said the Looker AI agent has “a handful of really promising use cases” in telecommunications. It highlighted three key use cases: instant detection and remediation, customer service and business performance..
The road to follow
Fully autonomous AI systems are still far. Tools like Looker’s conversational analytics still keep humans in the loop, Wurm noted, providing insights while leveraging Google’s data infrastructure and semantic insights.
Indeed, Bailis explained that even if agents like Looker’s evolve beyond simple question-and-answer models like the first versions of ChatGPT or Gemini, the industry is still “one or two generations of models” away. ‘seamless agent-to-agent collaboration. “Even with current models, there is still a lot to build,” he said. “These APIs and conversational analytics agents are only scratching the surface. »
However, Bailis said the progress made so far with AI agents will already have been considerable. “If we stopped today, their impact in the data space would still be enormous, comparable to that of cloud computing,” he said.
Bailis summarized the potential of agentic AI in data analysis: “Every business cares about three questions: What happened? Why does this happen? What happens next? It’s very painful to get these answers today. With this conversational data broker, once you define these metrics, you will be able to get these answers again and again.
