Google Cloud unveiled the Gemini agent at its annual Gemini at Work 2026 conference, positioning the system as a universal, single digital assistant engineered to handle enterprise knowledge work, run complex code and execute multi-step workflows straight from a prompt box. Nearly 500 enterprise customers each processed over one trillion tokens in the past year alone, according to Google Cloud. Furthermore, approximately 80% of all Google Cloud clients now deploy its artificial intelligence offerings, while nearly 90% of Fortune 100 enterprises actively use Gemini Enterprise.
With business environments pivoting toward agentic systems, Google stated that the new Gemini agent is built around delegating end objectives rather than micromanaging step-by-step instructions.
It integrates directly into developer command lines, Google Workspace, Microsoft 365, and Slack, or operates headlessly behind third-party enterprise tools. The Gemini agent is designed around six core architectural pillars. Omnipresent and headless deployment: The system functions across web interfaces, iOS, Android, macOS, and Windows.
Flexible multi-model routing: The agent decouples the user experience from the underlying foundation model. It dynamically routes queries to the most cost-effective and accurate engine available, navigating Google’s own Gemini family alongside Anthropic’s Claude models, with support for future private and open-source models.
It can also stand up permanent “coworker agents” assigned to specific team functions. Execution in the Cloud: Running entirely on cloud infrastructure, the agent retains a shared memory bank and personalization graph across all endpoints. Users can initiate a job that runs for hours or days, close their computer, and return to completed deliverables without re-briefing the system. Multi-agent orchestration: When handling complex projects, Gemini can spin up specialised, temporary sub-agents to tackle parallel or sequential tasks. These coworker agents receive their own dedicated email addresses (such as @agents.company.com), independent cloud storage, and role boundaries. Deep context: The system retains four distinct layers of memory: session memory for active jobs; semantic memory for company documentation; procedural memory for repeatable workflows; and episodic memory to recall past interactions.