Bloomberg reported today that Google has released a new line of AI agents aimed squarely at OpenAI’s and Anthropic’s enterprise lead. I’ve been waiting for this move — we’ve all been waiting for it — and the strategic story is more interesting than any single demo.
The state of play
Going into Q2 2026:
- Anthropic has the agentic-workloads lead. Claude’s tool-use reliability and MCP made them the default for production agent stacks.
- OpenAI has the consumer-product lead and the largest install base, but enterprise agent share is lagging.
- Google has the platform lead — Gemini 2.5 Pro’s 1M-token context, native multimodal, deep Workspace integration — but the agent product story has been thin.
This release is Google saying: we have the model, we have the data, we have the cloud — now we have the agents.
What I think the bet is
Google’s competitive moat in enterprise AI is integration with what enterprises already use: Gmail, Drive, Calendar, Docs, Sheets, BigQuery, Workspace. Anthropic has MCP (and the connector ecosystem on top). OpenAI has Operator and a growing tool catalogue. Google has the data the agent should be acting on, already in their cloud.
If the agents are even competent, the integration story alone makes them a serious enterprise play. You don’t need to build connectors for the data when the data lives in the same cloud as the agent.
Two things I’m watching
MCP support. Will Google’s new agents speak MCP, or push their own integration story? My bet — they’ll do both, badge MCP support as table stakes, and try to differentiate on Workspace/Cloud-native depth. The vendor-neutral protocol is too widely adopted to ignore at this point.
Enterprise SLAs. Anthropic’s enterprise lead is partly built on operational reliability — SLAs, predictable rate limits, a quieter incident history. Google’s enterprise track record on AI products is mixed (remember Bard?). The model is the easy part. Operating a model business at enterprise scale is the hard part.
The shape of the rest of the year
We now have three credible agent platforms competing for enterprise share, plus a healthy long tail (Microsoft via Azure + OpenAI, AWS via Bedrock, Salesforce, etc). For customers, this is excellent — pricing pressure is real, multi-vendor patterns become viable. For practitioners, it means the integration layer (MCP, agent frameworks, eval harnesses) becomes more important, not less.
Pick the platform that’s strong where your data lives. Build the integrations on the standard.
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