Google Cloud has introduced the Google Cloud Gemini agent, a single agent for enterprise work. The Gemini agent is a cloud-hosted agent from Google Cloud that answers questions, does knowledge work, creates media, and writes and runs code. It does all of this from 1 prompt box and 1 API. For developers, the agent is the product and the model is a routing decision.
TL;DR
- Runs on: Google Cloud (AI Hypercomputer). Reached from web, mobile, desktop, CLI, Workspace, Microsoft 365, Slack, or headless.
- Best: Bloomberg Media lifted SQL query accuracy by 63% by grounding data agents in Knowledge Catalog.
- Bottom line:
- Best: 1 governed agent with cloud memory, sub-agents and hard spend caps.
- Worst: buyers must evaluate it on customer anecdotes, not reproducible numbers.
What is the Google Cloud Gemini agent?
It is a delegation layer, not a chatbot. You give it objectives, not instructions. It plans the work, picks skills and tools, connects to company systems, and returns finished output.
Google lists 6 architectural principles:
- Unified agent: chat, autonomous objectives and code generation share 1 interface.
- Omnipresent access: any device or channel, plus embedding in third-party apps.
- Persistent execution: it runs in the cloud with 1 set of memories and 1 personalization graph. Jobs lasting hours or days keep running after you close the laptop.
- Multi-agent orchestration: it creates temporary sub-agents, each with its own identity, and runs parallel or sequential steps.
- Deeply contextual: it learns your tools, data and work history over time.
- Model choice flexibility: each job runs on the best-fit model.
How does the Gemini agent remember and reason?
It keeps 4 kinds of memory. Session memory covers the current task, even across days. Semantic memory is a structured knowledge base it builds from documents and people. Procedural memory stores how jobs get done, including skills it writes for itself. Episodic memory records everything it has done before.
Skills are modular prompts stored in a shared company registry. Tools come from an enterprise tools registry. Connectors cover Slack, Jira, Salesforce, ServiceNow, BigQuery, Snowflake, desktop files and any Model Context Protocol server.
Which models does it use?
Today it orchestrates across Google’s Gemini models and Claude models from Anthropic, with other private and open models planned. Google’s own lineup is Argon for frontier reasoning, Flash for speed and volume, Omni for generative media, and Gemma for open-weights edge work.
What are coworker agents?
A coworker agent is a persistent teammate with a defined role. In Workspace it receives its own account: email address, calendar, Drive and a directory entry. Colleagues @mention it in Chat or Docs, and its edits appear under its own name in version history. It sees only what is shared with it.
The agent also works inline across Gmail, Docs, Sheets, Slides, Chat and Calendar, and offers 1-click delegation of tasks it spots.
What does it add for data teams?
Data and ML engineers describe outcomes in plain language. The agent then writes PySpark code, provides notebooks, trains models and fixes pipeline issues. Business users get saved BigQuery reports that rerun without token costs.
Three services ground the answers. Knowledge Catalog maps business definitions once for every agent. Smart Storage enriches unstructured objects in place; Google says 90% of enterprise data is unstructured. Borderless Lakehouse queries Amazon S3 and Azure Data Lake with no variable egress fees.
