Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search

Google Cloud has partnered with Parallel Web Systems to natively integrate Parallel's search infrastructure as a web grounding provider on the Gemini Enterprise Agent Platform. This integration enables developers to anchor their AI agents in verifiable, real-time web results, significantly improving factual accuracy for complex enterprise workflows. Additionally, the partnership offers expanded architectural flexibility, allowing users to programmatically extract, permanently cache, and process web data alongside other large language models.

Building scalable AI agents with modular prompt transpilation

To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates. By running these modular "skill files" through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines. This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests.

Now available: group conversations with external collaborators in Google Chat

For many teams, it’s essential to be able to work in real-time with partners from outside your organization. We’re improving external collaboration in Google Chat by making it possible to create group conversations that include external users.

Previously, teams using Google Chat could only create 1:1 conversations with external users, or create a space in Chat with external access enabled.

With this update, it’s now possible to create group conversations with multiple external users, reducing friction and streamlining external collaboration. No additional configuration is required, and access for external users is enabled once they accept an invitation through an email notification. Group conversations with external members are labeled with a visible badge to help ensure that users are aware of the context for all conversations in Chat.


Getting started

  • Admins: External group DMs share the same admin controls as external spaces. Ensure that the External spaces & group direct messages admin setting is enabled for your organization to allow users to create or join external group DMs.
  • End users: If allowed by their admin, end users can add external users when they create a new group DM. External users can’t be added to existing group DMs that only have internal users. External users who are added to group DMs will receive an email invitation and must accept the invitation to participate in the group conversation.

Rollout pace

Availability

  • Available to all Google Workspace customers and Workspace Individual subscribers

Resources

Unlocking the Next Era of On-Device AI with Google Tensor and Pixel

At Google I/O Connect India, Google showcased the future of 100% private, on-device AI powered by the custom Tensor SoC and TPU for the new Pixel 10 family. The event debuted the lightweight Gemma 4 E2B model, which runs natively on the device to enable completely offline multimodal features like AI chat, real-time image recognition, and personal agent tasks. Developers can start building these secure, edge-based applications today by accessing the newly announced Tensor SDK beta and its accompanying open-source resources.

Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)

To serve the 397B-parameter Qwen 3.5 Mixture-of-Experts (MoE) model on Ironwood TPUs, engineers developed a modular JAX/Pallas optimization stack that achieved up to a 4.7x inference speedup for prefill-heavy workloads. The team bypassed severe hardware sharding constraints by deploying a hybrid Data Parallelism and Expert Parallelism (DP+EP) topology, paired with custom low-level communication fusions like a hierarchical reduce-scatter to optimize cross-device token routing. Finally, by executing hardware-aware custom kernels—such as Batched Ragged Page Attention and a fully-fused Gated DeltaNet (GDN) block—they successfully saturated HBM bandwidth and TensorCore MXUs to push system throughput near its theoretical roofline limits.