Google has been developing agentic AI for years, and the viral success of OpenClaw might finally tip the scales. For years, tech companies promised AI as a competent personal assistant but delivered more of a clueless intern. This is beginning to change, largely due to OpenClaw, a viral open-source AI agent platform. Among AI labs pursuing similar success, Google seems particularly well-positioned to scale its agent technology.
At I/O 2026, Google introduced new AI agents designed for tasks like gathering information, planning events, summarizing inboxes, and more, claiming these agents will integrate seamlessly into Google’s toolset and external applications. These continuous background agents build on features that have proven effective for OpenClaw’s success, amplified by Google’s extensive digital knowledge base.
Koray Kavukcuoglu, Google DeepMind CTO, noted in an interview that AI agents are transitioning from a research idea to a part of everyday life this year. The introduction of Gemini Spark marks a significant step, promising integration across Google services and over 30 external partners. Cloud-based, it operates 24/7, syncing across devices, with a beta debuting on Google’s Ultra plan next week.
Gemini Spark aims to perform everyday tasks such as shopping and coordinating schedules, with potential for custom uses. Google also introduces the Daily Brief, akin to OpenAI’s ChatGPT Pulse. If Gemini Spark operates as promised, it could revolutionize AI agents in tech companies.
Google’s AI search will incorporate agent capabilities this summer, executing background research tasks, while an expansion of the Antigravity platform develops autonomous agents. The release of Gemini 3.5 Flash promises enhanced capabilities and efficiency, with Kavukcuoglu indicating its strength in handling simultaneous tasks.
While Google lags behind OpenClaw’s innovation, its large-scale services may provide a competitive advantage, overcoming the cost pressures faced by dedicated AI firms. If Google successfully utilizes its resources, it could make AI agents practically useful. If it fails, it might necessitate reevaluating the approach.