Microsoft and Wiz caught 90% of bugs using AI agents. For teams juggling tight deadlines, this means fewer late-night fixes and more time for strategic work. Yet, the success of such tools depends on how well they fit into existing processes. As AI adoption grows, businesses must balance promise with practicality. How do you evaluate whether an AI tool aligns with your workflow without overcomplicating things?

𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐱𝐩𝐚𝐧𝐝𝐬 𝐀𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐆𝐞𝐦𝐢𝐧𝐢 𝐒𝐩𝐚𝐫𝐤, 𝐈𝐭𝐬 𝐍𝐞𝐱𝐭-𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 Google is broadening access to Gemini Spark, its agentic AI assistant, bringing powerful automation capabilities to more Google AI Pro and AI Ultra subscribers. The move marks another major step in the race toward intelligent AI agents that don't just answer questions—but actively complete tasks on your behalf. Powered by Gemini 3.5 and deeply integrated with Google Workspace, Gemini Spark can help prioritize emails, summarize lengthy conversations, draft responses, create reports, and automate multi-step workflows across Gmail, Docs, Calendar, and other connected services. The expansion signals a broader shift from traditional AI chatbots to always-on digital assistants capable of managing everyday work with minimal human intervention. As businesses increasingly embrace AI-driven productivity, agentic AI is quickly becoming the next frontier of enterprise transformation. https://lnkd.in/d3u7J-cu #Oman #Muscat #TheOmanToday #OmanVision2040 #Google #Gemini #GeminiSpark #ArtificialIntelligence #AI #GenerativeAI #AgenticAI #DigitalTransformation #EnterpriseAI #FutureOfWork #Productivity #CloudComputing #Innovation #Technology #TechNews #BusinessTechnology #TOT

Modern development and mission teams often lose time not to lack of tools, but to fragmented knowledge and disconnected systems. Our teams build secure, scalable GenAI assistants…that bring documentation, code examples, and AI-driven capabilities into a unified, conversational interface inside the workflow. Using RAG-enabled search and agent orchestration, we help teams move faster by reducing the time spent searching across repositories, support channels, and documentation systems. Instead, users can interact directly with trusted knowledge and automated agents in real time. The result is a standardized, secure, and extensible AI experience that improves developer productivity, reduces cognitive load, and accelerates how teams build, test, and deliver mission-critical capabilities. See how @Booz Allen is building secure, scalable GenAI assistants that transform how teams access knowledge and work with AI: https://bit.ly/4f4uHj0

Agent engineering is evolving fast. #AI agents become easier to test, debug, monitor, and scale. Google's Agent Development Kit (#ADK) 2.0 marks a major shift from prompt-driven agents to structured, production-oriented AI systems. Here's what changes: 🔷 Graph-based workflows replace prompt chaining for greater control. 🔷 Multi-agent coordination becomes an architectural feature. 🔷 Dynamic workflows introduce loops, conditionals, async logic, and automatic checkpointing. 🔷 Built-in observability, persistent sessions, and sandboxed execution bring enterprise readiness closer than ever. Read more 👉 https://lnkd.in/dqSi3rb9 #kartaca

A company got 98% of its engineers using AI. Productivity went up 10%. A Head of Engineering at a large fintech walked me through it. The rollout was a genuine success by every standard playbook: near-total adoption, real usage, engineers who like the tools. Most CTOs would kill for those numbers. Ten percent. And now the conversation inside the company has moved to token costs. Here's what I think is happening, because we see the same pattern in our own data across teams: Adoption gets everyone to the same starting line. An engineer with an AI assistant in "ask mode" is a slightly faster version of themselves. That's your 10%. The teams pulling away are doing something different: - Work split so agents can run in parallel, not assist one keystroke at a time - Specs and reviews restructured around agent output - Measurement based on shipped impact, not usage dashboards The old goal was getting your engineers to use AI. That goal is dead, everyone uses it now. The new goal is rebuilding how work flows through your team so the tools actually compound. 98% adoption means the rollout worked. It does not mean the organization changed.

AI is being added into business tools faster than most people realize. Teams try new features, integrations get switched on, and over time it becomes part of how work gets done. But if you don’t have a clear view of where AI is being used or who is responsible for it, responding to a problem becomes much harder… https://hubs.la/Q04kW-FP0

🔎 How do I automate a process without a long implementation? 🔎 How do I move faster without breaking what works? 🔎 How do I bring in AI without a rebuild? OCTO helps organizations automate processes around their existing systems—without writing code or disrupting operations. Explore how OCTO can help your teams move: faster: https://lnkd.in/g8y8B4CW

AI is being added into business tools faster than most people realise. Teams try new features, integrations get switched on, and over time it becomes part of how work gets done. But if you don’t have a clear view of where AI is being used or who is responsible for it, responding to a problem becomes much harder… https://lnkd.in/eSq7CTsT

3 agent-economy signals most founders are still underpricing. The AI race is shifting from smarter chatbots to governed, enterprise-ready agents. This is where @Anthropic, @OpenAI, @Google, and the open-source ecosystem are quietly competing now: not just model quality, but trust at deployment. → Enterprises do not buy “autonomy” first; they buy control, observability, and compliance. → Agentic coding and workflow tools are becoming the wedge because ROI is visible in days, not quarters. → The next moat may be less about the model and more about the orchestration layer around it. My take: the first $10B agent-native company will not look like a chatbot company. It will look like an operating system for work — with permissions, memory, audit logs, and humans in the loop. What layer of the AI agent stack do you think captures the most value: models, tools, orchestration, or distribution? 👇

AI agents are reshaping work, but organizations face a critical choice: buy agents from vendors, build them in-house, or manage the chaos of shadow AI spreading across the enterprise. The stakes are architectural, financial, and cultural. Organizations that win treat AI agent adoption as infrastructure governance work. They audit vendor capabilities against strategy, establish clear processes for evaluating and deploying agents, and build transparency around tool proliferation before shadow AI creates technical debt and compliance risk. Unmanaged AI adoption creates fragmentation. Poor vendor alignment creates lock-in. Weak governance creates shadow IT on steroids. At Converge IP, we help organizations evaluate the right approach—so AI agents accelerate work instead of creating organizational chaos. #ConvergeIP #AIAgents #ITStrategy #DigitalTransformation #CIOLeadership #EnterpriseArchitecture #Governance https://zurl.co/BaMQo