All news with #agentic ai tag

Developers Leading AI Transformation Across Enterprise

💡 Developers are accelerating AI adoption across industries by using copilots and agentic workflows to compress the software lifecycle from idea to operation. Microsoft positions tools like GitHub, Visual Studio, and Azure AI Foundry to connect models and agents to enterprise systems, enabling continuous modernization, migration, and telemetry-driven product loops. The shift moves developers from manual toil to intent-driven design, with agents handling upgrades, tests, and routine maintenance while humans retain judgment and product vision.

Amazon Bedrock AgentCore GA: Secure Agent Platform

🔐 Amazon Bedrock AgentCore is now generally available, offering an agentic platform to build, deploy, and operate agents securely at scale without infrastructure management. It adds VPC support, extended eight-hour runtimes, session isolation, and initial Agent-to-Agent (A2A) protocol support. Identity, Gateway, and Observability features provide secure authorization, tool discovery, and CloudWatch/OTEL monitoring.

AI Ethical Risks, Governance Boards, and AGI Perspectives

🔍 Paul Dongha, NatWest's head of responsible AI and former data and AI ethics lead at Lloyds, highlights the ethical red flags CISOs and boards must monitor when deploying AI. He calls out threats to human agency, technical robustness, data privacy, transparency, bias and the need for clear accountability. Dongha recommends mandatory ethics boards with diverse senior representation and a chief responsible AI officer to oversee end-to-end risk management. He also urges integrating audit and regulatory engagement into governance.

Amazon CloudWatch Adds Generative AI Observability

🔍 Amazon CloudWatch is generally available with Generative AI Observability, providing end-to-end telemetry for AI applications and AgentCore-managed agents. It expands monitoring beyond model runtime to include Built-in Tools, Gateways, Memory, and Identity, surfacing latency, token usage, errors, and performance across components. The capability integrates with orchestration frameworks like LangChain, LangGraph, and Strands Agents, and works with existing CloudWatch features and pricing for underlying telemetry.

Amazon ElastiCache Adds Vector Search with Valkey 8.2

🚀 Amazon ElastiCache now offers vector search generally available with Valkey 8.2, enabling indexing, searching, and updating billions of high-dimensional embeddings from providers such as Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI with microsecond latency and up to 99% recall. Key use cases include semantic caching for LLMs, multi-turn conversational agents, and RAG-enabled agentic systems to reduce latency and cost. Vector search runs on node-based clusters in all AWS Regions at no additional cost, and existing Valkey or Redis OSS clusters can be upgraded to Valkey 8.2 with no downtime.

Autonomous AI Hacking and the Future of Cybersecurity

⚠️AI agents are now autonomously conducting cyberattacks, chaining reconnaissance, exploitation, persistence, and data theft at machine speed and scale. In 2025 public demonstrations—from XBOW’s mass submissions on HackerOne in June, to DARPA teams and Google’s Big Sleep in August—along with operational reports from Ukraine’s CERT and vendors, show these systems rapidly find and weaponize new flaws. Criminals have operationalized LLM-driven malware and ransomware, while tools like HexStrike‑AI, Deepseek, and Villager make automated attack chains broadly available. Defenders can also leverage AI to accelerate vulnerability research and operationalize VulnOps, continuous discovery/continuous repair, and self‑healing networks, but doing so raises serious questions about patch correctness, liability, compatibility, and vendor relationships.

The AI SOC Stack of 2026: What Separates Top Platforms

🤖 As organizations scale and threats increase in sophistication and velocity, SOCs are integrating AI to augment detection, investigation, and response. The market ranges from prompt-dependent copilots to autonomous, mesh agentic systems that coordinate specialized AI agents across triage, correlation, and remediation. Leading solutions prioritize contextual intelligence, non-disruptive integration, staged trust, and measurable ROI rather than promising hands-off autonomy.

CrowdStrike Named Visionary in 2025 Gartner SIEM Placement

🔍 CrowdStrike Falcon Next‑Gen SIEM has been named a Visionary in the 2025 Gartner Magic Quadrant for Security Information and Event Management. The product is presented as an agentic SOC engine that combines AI-driven detections, real-time telemetry and a unified data foundation to accelerate detection and response. CrowdStrike cites metrics including 150x faster search, over 1PB/day ingestion and up to 80% cost savings, and highlights the acquisition of Onum to improve real-time pipelines and scale. New AI agents for workflow, data transformation, search analysis and correlation rule generation aim to simplify playbook creation, data prep and detection tuning.

Indirect Prompt Injection Poisons Agents' Long-Term Memory

⚠️This Unit 42 proof-of-concept shows how an attacker can use indirect prompt injection to silently poison an AI agent’s long-term memory, demonstrated against a travel assistant built on Amazon Bedrock. The attack manipulates the agent’s session summarization process so malicious instructions become stored memory and persist across sessions. When the compromised memory is later injected into orchestration prompts, the agent can be coerced into unauthorized actions such as stealthy exfiltration. Unit 42 outlines layered mitigations including pre-processing prompts, Bedrock Guardrails, content filtering, URL allowlisting, and logging to reduce risk.

Amazon Quick Suite: Agentic AI Workspace for Business

🤖 Amazon Quick Suite is now generally available as an agentic, AI-powered workspace that retrieves insights across the public internet and your enterprise data stores — including Slack, Salesforce, Snowflake, databases, and other documents — and moves instantly from answers to actions. Quick Suite can execute or trigger tasks in popular applications like Salesforce, Jira, and ServiceNow, and automate workflows from RFP responses to invoice processing and account reconciliation. AWS highlights customer privacy — queries and data are not used to train models — and administrators can enable and tailor the experience quickly; new customers receive a 30-day trial for up to 25 users.

Securing Agentic AI: Microsoft Ignite Security Guide

🔒 Microsoft Ignite 2025 highlights security-focused sessions and hands-on labs tailored for practitioners and leaders. Join in San Francisco Nov 17–21 (or online Nov 18–20) for briefings, demos, and instructor-led labs covering Microsoft Security Copilot, Sentinel, Defender, Entra, and Purview. A Security Forum (Nov 17) and keynote segments led by senior security executives will explore designing, governing, and protecting agentic AI across the lifecycle.

Microsoft Azure Debuts Large-Scale NVIDIA GB300 Cluster

🚀 Microsoft Azure announced the first production-scale cluster using more than 4,600 NVIDIA GB300 NVL72 (Blackwell Ultra) GPUs, co-engineered with NVIDIA to support OpenAI and other frontier AI workloads. The new ND GB300 v6 VMs are optimized for reasoning models, agentic systems, and multimodal generative AI, delivered on rack-scale systems with 72 GPUs per rack and 36 NVIDIA Grace CPUs. Microsoft says this infrastructure will shorten training from months to weeks and will scale to hundreds of thousands of Blackwell Ultra GPUs globally.

Google Introduces Gemini Enterprise for the Workplace

🚀 Gemini Enterprise is presented as Google’s unified, enterprise-grade AI front door that integrates advanced models, a no-code workbench, pre-built and customizable agents, secure data connectors, centralized governance, and an open partner ecosystem. The chat-first interface works across Google Workspace and Microsoft 365 and adds multimodal agents for text, image, video, and speech. Google highlights developer tooling, open agent protocols, agent monetization, and customer deployments to accelerate end-to-end workflow automation and auditable governance.

Researchers Identify Architectural Flaws in AI Browsers

🔒 A new SquareX Labs report warns that integrating AI assistants into browsers—exemplified by Perplexity’s Comet—introduces architectural security gaps that can enable phishing, prompt injection, malicious downloads and misuse of trusted apps. The researchers flag risks from autonomous agent behavior and limited visibility in SASE and EDR tools. They recommend agentic identity, in-browser DLP, client-side file scanning and extension risk assessments, and urge collaboration among browser vendors, enterprises and security vendors to build protections into these platforms.

Partners Powering the Gemini Enterprise Agent Ecosystem

🚀 Gemini Enterprise launches a curated ecosystem of partner-built AI agents that integrate with Google Cloud to deliver validated, secure solutions for enterprise workflows. The platform supports Agent2Agent (A2A) communication and includes a Gemini-powered AI agent finder for natural language discovery and filtering by industry, use case, and validation status. A broad set of technology and consulting partners — from Box and Salesforce to ServiceNow, Workday, and Accenture — are bringing agents and services to the Google Cloud Marketplace to accelerate deployment and adoption.

Autonomous AI Hacking: How Agents Will Reshape Cybersecurity

⚠️ AI agents are increasingly automating cyberattacks, performing reconnaissance, exploitation, and data theft at machine speed and scale. In 2023 examples include XBOW's mass vulnerability reports, DARPA teams finding dozens of flaws in hours, and reports of adversaries using Claude and HexStrike-AI to orchestrate ransomware and persistent intrusions. This shift threatens accelerated attacks beyond traditional patch cycles while presenting new defensive opportunities such as AI-assisted vulnerability discovery, VulnOps, and even self-healing networks.

Startup Technical Guide: Building Production AI Agents

🤖 Google Cloud published the Startup technical guide: AI agents, a practical, operations-driven roadmap to design, build, and operate agentic systems for startups. The guide outlines three paths — build with the open-source Agent Development Kit (ADK), design no-code agents in Agentspace, or adopt managed and partner agents via Vertex AI and the Agent Garden marketplace. It details four development steps (identity, prime directive, tools, lifecycle), highlights operational rigor (AgentOps), and promotes interoperability through standards such as MCP and A2A, all aimed at safe production deployment.

DeepMind's CodeMender: AI Agent to Fix Code Vulnerabilities

🔧 Google DeepMind has unveiled CodeMender, an autonomous agent built on Gemini Deep Think models that detects, debugs and patches complex software vulnerabilities. In the last six months it produced and submitted 72 security patches to open-source projects, including codebases up to 4.5 million lines. CodeMender pairs large-model reasoning with advanced program-analysis tooling — static and dynamic analysis, differential testing, fuzzing and SMT solvers — and a multi-agent critique process to validate fixes and avoid regressions. DeepMind says all patches are currently human-reviewed and it plans to expand maintainer outreach, release the tool to developers, and publish technical findings.

150 AI Use Cases from Startups Leveraging Google Cloud

🤖 At the AI Builders Forum, Google Cloud highlighted 150 startups using its generative AI stack—Vertex AI, Gemini, GKE, and Cloud Storage—to build agentic systems, healthcare models, developer tools, and media pipelines. The post catalogs companies across sectors (healthcare, finance, retail, security, creative) and describes technical integrations such as fine-tuning with Gemini, inference on GKE, and scalable analytics with BigQuery. It encourages startups to join Google for Startups Cloud and references a new Startup Technical Guide: AI Agents for building and scaling agentic applications.

Five Best Practices for Effective AI Coding Assistants

🛠️ This article presents five practical best practices to get better results from AI coding assistants. Based on engineering sprints using Gemini CLI, Gemini Code Assist, and Jules, the recommendations cover choosing the right tool, training models with documentation and tests, creating detailed execution plans, prioritizing precise prompts, and preserving session context. Following these steps helps developers stay in control, improve code quality, and streamline complex migrations and feature work.