All news with #agentic ai tag

🛡️ Recent developments show AI moving from automation to autonomous cyber operations, shifting how offense and defense interact. The Anthropic Mythos Preview and related incidents illustrate models discovering and chaining vulnerabilities with limited human direction, prompting coordinated defensive responses from major vendors. Policy and procurement are adapting, and security leaders must treat AI agents as principals, invest in adaptive defenses, and reframe risk models for continuous compromise.

🚀 AWS announces general availability of Neuron 2.30.0, delivering NKI 0.4.0 with Trainium3-specific hardware capabilities, 22 new NKI Library kernels, and expanded Neuron Agentic Development skills to aid model porting and validation. The release targets ML developers optimizing training and inference or porting models to AWS Trainium and Inferentia, and it is available in Regions supporting Trn1/Trn2/Trn3 and Inf1/Inf2 instances.

🛡️ Anthropic appears to be preparing a public rollout of its restricted Mythos model, which the company warned poses major security risks by automating high-quality cyberattacks. Announced in April as an advanced frontier model, Mythos showed dramatic improvements in code reasoning and autonomy compared to Opus 4.7. References briefly appeared in Claude Code and Claude Security, suggesting a controlled preview, while Anthropic builds guardrails and works with partners through its Glasswing initiative.

🛡️ Researchers argue enterprises must stop treating AI agents as trusted components and instead secure them as untrusted systems. The paper, authored by teams from Google, UC San Diego, UW–Madison and others, distills five systems-security principles—least privilege, tamper resistance, complete mediation, secure information flow, and human risk—and maps eleven real-world agent attacks to these violations. They caution that stacking ML guardrails is insufficient and propose research directions for separating instructions from data, verifiable least-privilege policies, and information-flow controls.

🦷 Movix built a custom agentic AI platform to address a severe shortage of skilled dental technicians and reduce costly remakes in aligner and appliance manufacturing. Using Google Cloud infrastructure, including Gemini Enterprise Agent Platform, Cloud Run with L4 GPUs, and Compute Engine, Movix developed deep learning, computer vision, and 3D mesh models to automate quality control and data entry. The solution integrates with legacy lab systems, anonymizes PHI for compliance, and targets large-volume labs to improve accuracy, speed, and cost savings.

🧭 Microsoft is piloting agentic AI in Edge for Business to streamline multi-step workflows like form-filling, site navigation, and cross-tab data gathering. A limited preview introduces a unified new-tab experience with calendar entries, files, and Copilot prompts to reduce context-switching. Enterprises can enforce data protections—blocking copy/paste, keeping prompts and responses inside their Microsoft 365 tenant, and auditing or blocking sensitive uploads. The features integrate with Purview to detect and prevent policy violations when users sign into Edge for Business.

🔒 Google is folding CodeMender into its broader Agent Platform strategy, expanding the AI-powered security agent from standalone vulnerability remediation toward an integrated, governed enterprise agent ecosystem. Launched in October 2025 to autonomously identify and patch vulnerabilities using Gemini models, CodeMender reportedly upstreamed dozens of fixes but lacks published performance metrics on accuracy and regressions. The integration emphasizes governance, observability, and identity, positioning CodeMender as a controlled participant in AI-native development and security pipelines rather than an unsupervised remediation tool.

🚀 At Google I/O ‘26, Google Cloud announced expanded agentic capabilities, new frontier models, and developer tools to help startups move from prototype to production. Highlights include Gemini 3.5 Flash and Gemini Omni for multimodal content, Antigravity 2.0 as an agent control plane with CLI/SDK and dynamic subagents, and Managed Agents to run agent workloads securely in Google Cloud. The releases aim to accelerate development, reduce infrastructure overhead, and provide enterprise-grade security.

🛡️ This week's ThreatsDay bulletin highlights a string of notable cybersecurity developments, from 47 zero-day exploits revealed at Pwn2Own Berlin 2026 to active Linux rootkit evolution. It summarizes warnings about agentic AI, targeted intrusions using AI agents, and advisories on token and dependency leaks. The report also covers nation-state tensions, ransomware activity, encrypted communications, and campaigns abusing identity recovery flows.

🔒 Amazon explains how AgentCore Gateway enforces a centralized authorization layer between autonomous agents and external tools, treating the LLM as an untrusted actor. Policies are expressed in the open-source Cedar language for readability, bounded execution, and mathematical analyzability, enabling deterministic enforcement and formal verification during policy authoring and attachment. A neuro-symbolic workflow translates natural-language rules into Cedar, validates them with Cedar Analysis, and enforces decisions at runtime to constrain tool invocations and filter unavailable actions.

🚀 Google Cloud announced general availability of GKE Agent Sandbox and introduced the open-source Agent Substrate. Agent Sandbox is a cloud-native execution environment designed for AI agents, offering pod snapshots to suspend idle workloads, an integrated warm pool for sub-second provisioning, gVisor and pluggable kernel isolation, and standby suspended VMs to reduce warm-pool cost. Agent Substrate aims to provide a minimal control plane and scheduler optimizations to support ultra-dense, low-latency agent workloads at scale.

🔧 Google today introduced Agent Executor, an open-source runtime standard for durable, resumable, and distributed agent execution. It offers event logging and snapshotting to enable durable execution, secure sandbox isolation to limit harm, and a single-writer architecture to maintain session consistency. Agent Executor also supports connection recovery so clients can reconnect to long-running workflows. The project is available in preview and pairs with Agent Substrate to improve Kubernetes-scale agent scheduling.

🔒 Microsoft has open-sourced two engineering tools—RAMPART and Clarity—to make agent safety a continuous part of development. RAMPART provides a pytest-style framework that brings red-team and adversarial tests into CI, evaluating tools invoked and side effects. Clarity is a structured design companion that captures problem statements, failure analyses, and decisions in a .clarity-protocol directory. Both aim to create living safety artifacts integrated into normal workflows.

⚠️Orchid Security's Identity Gap: Snapshot 2026 reveals that unseen, unmanaged identity elements now exceed visible ones, with 'identity dark matter' at 57% versus 43%. The report warns that rapid adoption of Agent AI amplifies risk because autonomous agents look for the most efficient access paths, often exploiting hard-coded or orphaned credentials and excessive privileges. Orchid urges strengthening identity and access management controls and using its readiness checklist to mitigate exposures.

🔧 At Google I/O, Google Cloud introduced a unified developer toolkit that brings Antigravity 2.0, the Gemini Enterprise Agent Platform, the Managed Agents API, and ADK 2.0 into a shared protocol layer to accelerate local development and secure cloud deployment. The post outlines a four-rung ladder from low-code Agent Studio to code-first ADK, all underpinned by the interoperable A2A protocol. New Antigravity desktop and CLI tools provide a consistent harness for coding agents, while Managed Agents offer agent-as-a-service with sandboxed execution. The platform emphasizes governance, skill reuse, evaluation tooling, and secure pipelines for production.

🤖 Today at Google I/O, Google Cloud announced a broad set of AI advances delivered through Gemini Enterprise and Google Workspace, including Gemini 3.5 Flash, Gemini Omni, Antigravity, and Gemini Spark. These offerings include new models, an Agent Platform with a Managed Agents API, and CodeMender for automated code security. The updates emphasize agentic workflows, multimodal content creation, enterprise-grade security, and faster, cost-efficient model performance.

🚀 Cloudflare and Anthropic have integrated Claude Managed Agents with Cloudflare Sandboxes, allowing teams to run the Claude agent loop on Anthropic while Cloudflare executes code, secures connections, and provides detailed observability. A default deployment template offers enhanced security through customizable outbound proxies, sandbox metrics and logs, SSH access, and configurable sandbox images. You can choose traditional microVMs or lightweight V8 isolates to optimize for performance and cost, and use Cloudflare Mesh or Workers VPC to connect agents to private services without exposing them to the Internet.

📈 Digital.ai's 2026 Application Security Threat Report found that 87% of monitored customer-facing apps were attacked in 2026, up sharply from 55% in 2022. The firm says agentic AI has lowered the skill and time required for threat actors to inspect code, generate exploits and adapt malware. Financial services, automotive and medical device apps were most targeted, and iOS attacks have nearly closed the gap with Android.

🔒 This article outlines five architectural patterns for exposing enterprise data to autonomous systems on Google Cloud, using BigQuery examples and mocked CRM data as pedagogical blueprints. It contrasts deterministic, developer-authored SQL APIs with agentic approaches that use LLMs, platform-native reasoning like the Conversational Analytics API, and the vendor-neutral Model Context Protocol (MCP). It highlights trade-offs in trust, complexity, cost, latency, and maintenance.

🚀 Google Cloud announced streaming AI enhancements to its Agentic Data Cloud at Next ‘26, unifying Pub/Sub, Dataflow, BigQuery, Bigtable and Managed Service for Kafka to deliver real-time context and low-latency inference. These additions include Pub/Sub AI inference, BigQuery continuous queries for stateful stream processing, Pub/Sub→Bigtable subscriptions, and unified embedding sinks for immediate semantic search and agent memory. The platform also supports MCP and ADK integrations so agents can manage resources and run inside Dataflow pipelines, reducing context lag for use cases like fraud detection and autonomous supply chain actions.