Long-context multimodal models · multi-agent protocol (A2A) · deep Workspace integration. Best-in-class for long-context and multimodal; fewer cross-vendor integrations than Claude or GPT.
Where Google is going with agents
Google’s strategy is long-context, multimodal, and Workspace-integrated. Four products that matter:
- Gemini 2.5 Pro at the model layer — 1M-token context window (some workloads run at 2M), strong multimodal capability, good tool-calling.
- Vertex AI Agents as the developer platform — agent builder, evaluation, deployment. Sits on GCP infrastructure.
- A2A (Agent-to-Agent) as Google’s protocol contribution — for multi-agent orchestration where one agent delegates to another. Complementary to MCP, not a replacement.
- Notebook LM as the consumer-facing show-piece — long-context document understanding for everyday users.
Google’s positioning has moved from “we’re behind” (early 2024) to “we lead in long-context and multimodal” (2026). For tasks where those matter — analysing whole codebases, summarising hour-long meetings, processing video — Google is the right default. For everything else, the field is closer than vendor decks suggest.
Where they lead
- Long-context. 1M tokens means you can put a whole codebase or a whole project’s docs in a single prompt. Recall above ~200k drops noticeably (test for your use case), but the ceiling is genuinely higher than Anthropic / OpenAI.
- Multimodal in / out. Image, audio, video — first-party, well-integrated. Notebook LM’s “podcast from your docs” feature is the most concrete demo of how multimodal compounds.
- Workspace integration. Gmail, Drive, Docs, Calendar, Meet — Gemini sees them all. For users who live in Google Workspace, the agent has structural visibility no other vendor matches.
- A2A protocol. Important if you’re orchestrating multiple agents. Open spec. Vendor-neutral in design (any agent can implement A2A — not just Gemini-based ones).
Where they lag
- Tool-calling reliability on agentic loops. Real-world tool-call agents on Gemini still trail Claude and GPT-5 in 2026. The model can do it; the consistency under load is the gap. Test in your stack before committing.
- Fewer cross-vendor host integrations. Cursor, Cline, Goose, Continue all came up with Claude / GPT-5 first. Gemini integration arrived later and is sometimes second-class. Improving but real.
- Smaller MCP server catalog. Most MCP servers had Claude-shaped tooling first; Gemini-aligned servers exist but the catalog is thinner.
- Vertex AI lock-in. If you build on Vertex Agents, your deployment is GCP-specific. Models are portable in concept, but the platform is not. Anthropic’s API is portable in a way Vertex isn’t.
Honest take
Google’s the right default when your task is genuinely long-context or genuinely multimodal, or when your users live in Google Workspace. For everything else, the field is closer than vendor positioning suggests, and Anthropic / OpenAI offer cleaner builder experiences with stronger cross-vendor interop.
A2A is worth tracking even if you’re not building multi-agent today — it’s the most coherent protocol for that shape, and the agentic field is converging toward more multi-agent patterns over time.
Further reading
- Gemini — gemini.google.com
- Vertex AI Agents — cloud.google.com/vertex-ai/generative-ai/docs/agent-builder
- A2A protocol — search for “agent-to-agent protocol Google” for current spec
- Notebook LM — notebooklm.google.com
- AI Studio — aistudio.google.com
Last reviewed 2026-05-07. Next quarterly review due 2026-08-07.