All news with #agentic ai tag

🚀 The preview release of a fully managed, remote MCP server for BigQuery (Jan 2026) lets developers connect LLM-powered agents directly to analytics data via a standard HTTP endpoint without managing infrastructure. The blog demonstrates step‑by‑step integration with the Agent Development Kit (ADK) and the Gemini CLI, including OAuth client creation and Gemini API key setup, and loading a sample cymbal_pets dataset. It highlights compatibility with popular frameworks (ADK, LangGraph, Claude code, Cursor IDE) and reminds readers to follow AI security and production best practices.

🔍 As organizations embed AI and distribute workloads across cloud and edge environments, traditional security tooling increasingly misses hidden misconfigurations, inconsistent controls, and emergent AI-agent behaviors. Experts advise moving from reactive, tool-stacked approaches to a unified visibility strategy that normalizes telemetry, aligns people/processes/data, and continuously evaluates agentic behavior. Practical steps include using existing FinOps metrics, tagging, and cross-team audits to reveal anomalies, and applying AI-driven automation to integrate and extend current investments. A modern CMDB and enterprise knowledge graphs provide the contextual backbone needed for AI to correlate signals and surface risk without expanding the security stack.

🔐Agentic AI introduces a new class of identity that behaves with humanlike intent yet scales and persists like machines. Traditional IAM and PAM were designed for employees and predictable workloads; AI agents are decentralized, easy to create, cross‑platform, and often granted broad privileges, creating serious blind spots. CISOs should apply lifecycle management: assign clear ownership tied to the identity provider, define explicit measurable purpose and scope, enforce least privilege, maintain continuous visibility to detect privilege drift, and automate revocation when agents go idle.

🔒 As AI hype settles, CISOs are refocusing 2026 priorities on resilience, rapid detection, and measurable outcomes. They favor engineering-driven architecture for cloud stability, AI-enabled orchestration to cut dwell time, and broad identity and privilege governance for human and non-human accounts. Visibility and SaaS discovery will curb shadow AI use, while security baked into agentic AI and post-quantum preparedness (cryptographic inventories and vendor roadmaps) become essential. Turning security into a visible trust signal and linking spend to ROI rounds out the agenda.

🛡️ OWASP published the Agentic Applications Top 10 for 2026 to classify risks unique to autonomous AI agents. Koi Security summarizes multiple real incidents from the past year — malicious MCP servers, poisoned assistants, and RCEs in Claude Desktop extensions — that show how autonomy expands attack surfaces. The report stresses inventorying runtime dependencies, enforcing least privilege, and monitoring agent behavior to detect and contain attacks.

🔒 Many enterprises are racing to deploy autonomous agentic AI without establishing robust identity and authentication controls, creating an identity crisis for CISOs. Experts warn that fewer than 5–10% of organizations assign formal agent identities (for example via PKI) before wider release, leaving deployments vulnerable to hijacking and prompt-injection. Because agents routinely communicate with one another, a compromised agent can cascade malicious instructions across legitimate agents before revocation, and current vendor solutions and kill switches are incomplete or absent.

🔒 The post summarizes a recent Agent Factory episode where Google product leaders discuss running agentic workloads on GKE. It highlights the Agent Development Kit (ADK), containerized deployments to Artifact Registry, and why Kubernetes provides governance and fine-grained control for large-scale agents. Google demonstrated an Agent Sandbox using gVisor and strict network policies, and introduced Pod Snapshots to cut sandbox startup from minutes to seconds, enabling lower-latency, secure agent workflows.

🚀 These hands-on labs guide developers through three Google Cloud deployment options to move AI agents from local prototypes to production. The Vertex AI Agent Engine offers a fully managed, Python-optimized runtime that handles execution, memory, and tool invocation. Cloud Run provides a serverless container experience with autoscaling and language flexibility, while GKE delivers orchestrated control for microservice deployments.

🛡️ The OWASP Top 10 for Agentic Applications identifies the most critical security risks from AI agents—systems that access data, invoke tools, and act autonomously—and offers CISOs practical threat taxonomies, mitigation strategies, and example threat models. Contributors prioritized data-driven, real-world issues discovered during research, including many agentic deployments unknown to IT and security teams. The list is designed to be consumable and directly actionable for threat modeling, governance, and security architecture.

🔬 Google is partnering with the DOE's Genesis Mission to accelerate federally funded scientific discovery by combining high-performance computing, experimental facilities, and AI. Gemini for Government and Google DeepMind tools offer multimodal reasoning, agentic workflows, and an AI co‑scientist to speed hypothesis development. Google Cloud provides the secure, accredited infrastructure for multi‑lab deployments.

🚀 Microsoft was named a Leader in the 2025 Gartner Magic Quadrant for AI Application Development Platforms and is positioned furthest for Completeness of Vision. The post presents Microsoft Foundry as a unified platform to build, deploy, and govern agentic AI—emphasizing secure grounding, multi-agent orchestration, observability, and cloud-to-edge model deployment. It also describes an agent-driven submission process that automated evidence collection and validation to improve accuracy and efficiency.

🔒 The Palo Alto Networks State of Cloud Security Report 2025 warns that rapid enterprise AI adoption has massively expanded the cloud attack surface, with 75% running AI in production and 99% reporting at least one AI-targeted incident last year. It finds GenAI-assisted coding accelerating insecure code into production and AppSec teams unable to keep pace with weekly deploys. The research highlights rising API attacks, persistent identity weaknesses, and widespread tool sprawl, and argues for agentic security to unify cloud and SOC operations.

🔗 To build production-ready agentic systems, Google Cloud offers hands-on labs that demonstrate how Agent Development Kit (ADK), the Model Context Protocol (MCP), and the Agent-to-Agent Protocol (A2A) work together. The labs begin with a foundational "Hello World" agent and progress to connecting agents to knowledge sources via MCP, with concrete examples for exposing BigQuery and CloudSQL. By adopting these standards instead of bespoke integrations, teams can scale and maintain multi-agent systems more reliably.

🔒 The Cybersecurity Forecast for 2026 highlights how agentic security automation and widespread AI will reshape defenses, shifting SOCs from monitoring to automated action. It calls for building workforce AI fluency, evolving IAM to treat agents as managed identities, and deploying model-protection measures alongside tamper-proof backups. Boards will increasingly demand operational resilience, quantified exposure, and mature AI governance.

🔍 CrowdStrike has expanded exposure management with Falcon Exposure Management, merging continuous telemetry, AI-driven prioritization, and a unified Risk Knowledge Base to reduce noise and accelerate remediation. The Exposure Prioritization Agent reasons in real time about exploitability, environment-specific preconditions, and business impact to deliver actionable “fix first” recommendations. AI Discovery surfaces LLMs, MCP servers, and AI agents to map the emerging AI attack surface and associated risks, integrating natively with Falcon telemetry and SOAR workflows.

🤖 Brave has begun testing an agentic AI browsing mode that uses its privacy-focused assistant Leo to perform autonomous tasks like web research, product comparison, promo-code discovery, and news summarization. The feature is currently available in Brave Nightly and is disabled by default. Brave isolates the agent in a separate profile without access to cookies, logins, or sensitive data and adds restrictions plus an alignment checker to mitigate prompt-injection and other risks.

🤖 GPT-5.2 is now generally available in Microsoft Foundry, positioned as a reasoning-first foundation model for enterprise applications. It advances GPT-5.1 with deeper logical chains, expanded context handling, and agentic execution to produce shippable artifacts—design docs, runnable code, tests, and deployment scripts—with fewer iterations. The release emphasizes integrated enterprise controls, managed identities, and policy enforcement to support secure, governed adoption.

📈 This article describes a multi-agent business forecasting application developed by Google Cloud and App Orchid. The design pairs a Google prediction agent (leveraging TimesFM and the Population Dynamics Foundation Model) with an App Orchid Data Agent that builds a semantic knowledge graph and prepares AI-ready time-series. A forecasting orchestrator uses the A2A Protocol and Google’s ADK to route queries, automate data wrangling, run predictions on Gemini-powered Vertex AI, and return unified forecasts with enterprise-grade security and governance.

🔒 Microsoft argues that as organizations adopt agentic AI, security must be a strategic priority that enables growth, trust, and continued innovation. The post identifies risks such as oversharing, data leakage, compliance gaps, and agent sprawl, and recommends three pillars: prepare for AI and agent integration, strengthen organization-wide skilling, and foster a security-first culture. It points to resources like Microsoft’s AI adoption model, Microsoft Learn, and the AI Skills Navigator to help operationalize these steps.

🚀 Microsoft Ignite 2025 showcased a suite of Azure and AI updates aimed at accelerating production use of agentic systems. Anthropic's Claude models are now available in Microsoft Foundry alongside OpenAI GPTs, and Azure HorizonDB adds PostgreSQL compatibility with built-in vector indexing for RAG. New Azure Copilot agents automate migration, operations, and optimization, while refreshed hardware (Blackwell Ultra GPUs, Cobalt CPUs, Azure Boost DPU) targets scalable training and secure inference.