All news with #gemini tag

🚀 Google has integrated Antigravity into eligible Gemini Enterprise subscriptions, providing built-in administrative, spend, and security controls alongside new IDE extensions for VS Code and other editors. Administrators can manage pooled quotas, granular spend thresholds, overage options, and centralized usage metrics from the Gemini Enterprise console. The offering aligns Antigravity with Google Cloud's security, audit logging, and data privacy protections while supporting Workforce Identity Federation and ADC for seamless developer access.

🚀 This technical guide explains Google Cloud’s Managed Service for Apache Spark, contrasting traditional managed clusters with serverless deployment modes and execution models (interactive sessions and batches). It covers resource and cost optimization techniques including history-based autotuning, tuning cores/memory, dynamic allocation caps, and shuffle partition sizing. The article also demonstrates integrated troubleshooting using Gemini Cloud Assist to diagnose runtime failures and generate resilient PySpark fixes.

🗂️ Box and Google Cloud are integrating Gemini Multimodal Embeddings 2 into Box's Agentic Platform to extend RAG beyond text and enable unified search and reasoning across documents, images, tables, and charts. This integration preserves spatial and visual structure, supports crossmodal retrieval, and bridges heterogeneous formats like .docx, .xlsx, .pdf, and .pptx. The combined system targets use cases in finance, healthcare, and enterprise auditing by enabling layout-aware embeddings, cross-file synthesis, and visual-to-text auditing.

🔍 This article demonstrates a hybrid streaming pattern that pairs lightweight, CPU-based inference with downstream generative AI agents using Google Dataflow and the Agent Development Kit (ADK). It outlines an Apache Beam pipeline that filters routine events locally and routes only complex cases to a Gemini-backed agent for multi-step remediation, reducing API costs, latency, and quota exhaustion. The approach preserves a static DAG while enabling dynamic runtime branching for targeted automation.

🚀 Gemini in Google Cloud's Database Migration Service streamlines conversion of stored procedures, triggers, and functions from proprietary dialects like PL/SQL and T-SQL into PostgreSQL PL/pgSQL. The service analyzes full schema context, provides side-by-side diffs and inline explanations, and enforces IAM-bound security. Teams can validate, edit, and deploy converted code within a single console to shorten migration timelines.

🧭 This announcement explains how Looker’s governed semantic layer now integrates with Gemini Enterprise, enabling analysts to publish conversational agents via the Agent-to-Agent protocol. The integration routes KPI requests to Looker agents that generate deterministic SQL, preserving version-controlled business logic and existing row- and column-level access controls. It uses a zero-risk pass-through architecture with one-time OAuth consent so Gemini does not ingest or persist enterprise data. Features include native interactive charts, interoperability with other agents, and identity-centric authentication to maintain governance while delivering secure, conversational analytics.

🧭 This summer Google Cloud offers a free, hands-on training path powered by Gemini Enterprise Agent Ready (GEAR) to help developers and IT leaders move autonomous agents to production. The program includes sequential courses and skill badges covering agent fundamentals, multi-agent orchestration, ADK engineering, memory and state management, human-centered design, and operationalization on Google Cloud. Participants can earn credentials, access labs and prototypes, and join an All Things Agentic Hackathon with prizes to demonstrate real-world skills.

🤖 Google Cloud announced two AI-powered database agents — the Database Onboarding Agent for Day 0 setup and the Database Observability Agent for Day 1/2 monitoring and remediation — introduced as part of the Agentic Data Cloud at Google Cloud Next ‘26. These always-on agents integrate with Chat, CLI, the Cloud console, MCP servers, and IDEs to automate provisioning, configuration, telemetry correlation, root-cause analysis, and validated remediations. The Observability Agent leverages Gemini and multiple telemetry sources to surface fleet-level insights, in-product investigations, and actionable fixes, while the Onboarding Agent recommends appropriate database types and configurations based on application requirements. Supported services include AlloyDB, Bigtable, Cloud SQL, Firestore, Memorystore, and Spanner, and capabilities are available via Gemini Cloud Assist and select previews.

🛡️ Cisco Talos found criminals bypass commercial AI safety controls by fragmenting malicious tasks across multiple sessions and files, so no single request appears harmful. Their corpus included prompt logs from assistants like Claude Code, Codex, Cursor and Gemini, and guardrails generally provided little protection. Actors also used ownership claims, CTF labels and persistent memory to gain authorization, while skill level determined how effective AI-assisted campaigns became.

🔍 Google Cloud outlines an AI-accelerated, iterative approach to mainframe modernization that avoids risky "big bang" migrations. The strategy combines Gemini-based code reasoning with mainframe-specific tools across four pillars: assessment, modernization, de-risking, and data migration. Key capabilities include the Mainframe Assessment Tool for dependency mapping and business rule extraction, agentic modernization workflows for rewrite or deterministic modernization, Dual Run for live validation, and a Mainframe Connector for data migration.

🤖 Voicify partnered with Google Cloud to transform phone calls into reliable, AI-driven interactions for restaurants and healthcare. By adopting Gemini Enterprise and Vertex AI, the company improved latency, reduced costs, and achieved enterprise-grade security and compliance. Their orchestration platform validates orders against POS systems, handles traffic spikes with provisioned throughput and pay-as-you-go, and shortened client onboarding dramatically. The architecture emphasizes scalability, data integrity, and multicloud availability.

🛡️ Google has made CodeMender available as a fully managed AI code security agent for enterprise customers via the Gemini Enterprise Agent Platform and AI Threat Defense. Originally a DeepMind research project, CodeMender now builds and runs PoC exploits in sandboxes, proposes tested fixes into pipelines, and offers multiple Gemini model options for cost and coverage balance. Features include secure traffic routing, data isolation, zero code retention and integrations with tools like VS Code and Antigravity.

🔒 DeepMind has released Gemini 3.5 Flash Cyber, a lightweight AI specialized in rapid vulnerability discovery, validation, and patching. The model is available only to governments and trusted partners via the CodeMender pilot program and is designed for high-speed, low-cost scanning of code paths. DeepMind reports it outperforms other Gemini variants and rival models in finding unique, confirmed issues across complex projects.

🛡️ CodeMender is a managed code security agent now available in preview, offering automated scanning and remediation using Google DeepMind–tuned models via the Gemini Enterprise Agent Platform or as part of AI Threat Defense. It prioritizes fixes by exploitability, runs proof-of-concept exploits in customer-managed sandboxes, and generates validated patches that integrate into developer workflows. The agent supports multiple languages, integrates with CI/CD and IDEs, and enforces enterprise-grade governance and data controls.

🔒 Google is fixing a vulnerability that lets an attacker with physical access to a locked Android 16 device use Gemini to send SMS and WhatsApp messages without entering a PIN. Reports since May show the bypass exploits Gemini's Deep Research and a specific multi-touch gesture to circumvent authentication. Google says a patch is imminent; meanwhile, users should restrict Gemini's lock-screen access to limit exposure.

🔎 This post introduces 13 code-first demos for the Gemini Enterprise Agent Platform, showing how to build, scale, govern, and optimize agents using the ADK and Agents CLI. The demos range from an ADK foundation codelab and MCP data connectors to stateful deployment on Agent Runtime, event-driven long-running workflows, and production-grade governance with Agent Gateway and Model Armor. Each demo teaches practical patterns — from UI generation and multi-language A2A pipelines to test-driven security, AutoRater evaluations, and cross-framework orchestration — so teams can prototype locally and then deploy and monitor agents at enterprise scale.

🔎 The European Commission has ordered Google to grant rival AI assistants the same access to Android sensors and system features that Gemini enjoys, including camera, microphone, screen contents, background controls, and wake-word activation. Google must deliver the changes in the next major release, Android 18, or by 1 August 2027, with some concurrent hotword features delayed until Android 19. The decision, adopted under the Digital Markets Act on 16 July, also requires Google to provide anonymised Search query datasets to competing search engines and AI chatbots under strict safeguards and cost-based fees. The measures define a mix of restricted features requiring certification and open features available to all third-party apps, set up a Qualified AI Assistant Programme, and impose timelines and audit and anonymisation conditions.

🧭 Optimizing token consumption keeps AI coding assistants fast, accurate, and cost-effective. The guide recommends starting with default models like Gemini 3.5 Flash, using structured SKILL.md and AGENTS.md practices, and creating simple local tools for repetitive tasks. It emphasizes tiered workflows—high-reasoning planning followed by lean execution—checkpointing often, automating testing early, and avoiding context bloat and costly supervisor loops.

🎬 As part of an internal generative media hackathon, Google tested whether teams of AI agents could collaboratively produce short films using Scion, an open-source agent orchestration testbed. Each crew had three role-specific agents (Idea Person, Technical Lead, Editor) plus coach and coordinator agents, following a seven-step filmmaking pipeline with verification gates. Agents called multiple Google AI models via a shared CLI toolkit genmedia (Gemini, Veo 3.1, Lyria 3, Gemini Flash TTS) to generate images, video, audio, and music, producing over 25 productions and about 44 minutes of final footage. Teams found that shared files provided resilience, specific prompts and style choices improved results, and coach-led gates helped ensure completed deliverables.

🔎 Upgrading foundation models is slow and costly for engineering teams, often requiring months of manual testing and evaluation. Google Cloud Applied ML built an agentic workflow that reduces migration time from months to hours using the Gemini Enterprise Agent Platform and Google Antigravity. The blog outlines three lessons and practical steps—deploying Autoraters, building an agentic loop, and automating orchestration—to replace manual toil with intelligent automation.