Advancing AI Development

The AI race isn’t just about smarter models anymore—it’s about who controls the silicon and the stack. Google, NVIDIA, and a shifting center of gravity Google’s Gemini 3 launch, backed by in-house Tensor ASICs, has forced even Nvidia and OpenAI to publicly tip their hats—an unusual moment of mutual acknowledgement in a fiercely competitive market. At the same time, Google’s stock jumped while Nvidia’s dipped, underscoring how capital markets are already repricing what “AI leadership” might look like when hyperscalers own more of the hardware narrative. ASICs vs GPUs: control vs versatility Nvidia and AMD still dominate with GPUs that serve broad, complex workloads and are wrapped in a mature software and data center ecosystem that is very hard to displace. Google’s Tensor chips, as ASICs, trade that general-purpose versatility for efficiency on narrower, highly-optimized AI tasks—enough to attract interest from Meta and Anthropic, but not yet enough to unseat Nvidia’s platform-scale advantage. Ecosystems, not winners, will define value Gemini 3 now tops many public benchmarks across text and image tasks, but other models outperform it on search and specialized use cases—a reminder that “best model” is becoming context-dependent. The more interesting story is ecosystem interdependence: Google is both a rival and a major Nvidia customer, and enterprises are increasingly assembling multi-model, multi-cloud, multi-chip strategies rather than betting on a single winner. What this means for leaders For executives, the real strategic questions are shifting from “Which model is best?” to: ⚫ Where do we need tight vertical integration (data + model + chip) versus flexible, multi-vendor optionality? ⚫ How do we avoid over-dependence on a single GPU vendor while not underestimating the cost of moving away from a mature platform? ⚫ Which workloads justify ASIC-style optimization, and which demand GPU-style breadth and agility? If your current AI roadmap doesn’t explicitly address hardware strategy, ecosystem risk, and a multi-model future, it’s time to revisit it. Bring your product, infra, and finance leaders into the same room and pressure-test your AI stack assumptions for the next 3–5 years—before the chip layer, not the model layer, becomes your biggest strategic constraint. Read More 👉 https://lnkd.in/g7C5nzd2 #AI #GenAI #GoogleGemini #Nvidia #AIChips #CloudComputing #Developers #AIInfrastructure #TechStrategy #EnterpriseAI

A company I know deployed an AI agent in 3 days. No boundaries defined. No guardrails. No sandbox testing. No failure playbook. Week 1: It sent 400 unapproved emails to clients. This is not a horror story. This is what happens when excitement outpaces engineering. The companies succeeding with AI agents in 2026 all follow the same principle: Scaling follows confidence, not excitement. They start small. They define limits. They test adversarial scenarios. They build human approval gates. They observe before they expand. Here’s the step-by-step deployment path serious teams follow - Start with a safe, low-risk use case - Define the agent’s boundaries clearly - Map structured workflows (no guessing) - Ground it with trusted data sources - Apply least-privilege access - Add guardrails before autonomy - Choose the right architecture - Test in simulation (normal + edge cases) - Deploy in a sandbox first - Introduce human approval gates - Add observability and monitoring - Roll out gradually - Create a failure playbook - Build continuous learning loops - Implement governance & compliance controls Safe AI isn’t about slowing down innovation. It’s about engineering trust. Constrain → Ground → Test → Observe → Expand. 15-step framework. Swipe through. Your team needs this before the next sprint planning meeting. What’s the biggest mistake you’ve seen in AI agent deployment? Drop it below 👇

Building AI Agents is more than just normal Plug-and-Play It needs a dedicated development strategy. Let me explain... 📌 Along with just basic things like Frameworks and LLMs, developing agents is more about: - Understanding the wider scope for your use case. - Considering stakeholders' decisions and much more. 📌 Here’s a condensed development process we use based on industry reports and hands-on experience: 1. Planning - Draft Business Needs: Identify the core business problems and stakeholders' decisions for building the AI Agent. - Define Agent Objectives: Clearly specify the goals and intended capabilities of the AI agent. - Resource Allocation: Assign necessary resources like personnel, budget, and compute infrastructure. - Risk & Ethics Review: Assess potential ethical concerns and compliance risks. 2. Design - Design Guardrails: Architect limits and constraints to prevent unintended behavior. - Choose a Framework: Select the right development framework. - Select a Model Based on Workflow: Pick a model architecture. - Grounding with Context: Integrate domain knowledge and relevant data into the agent design. 3. Development - Build the Agent Logic: Implement the core logic, rules, or reasoning system. - Integrate the Models: Plug in the selected AI/ML models. - Fine-tune Models (If Required): Refine pre-trained models using specific datasets to improve accuracy. - Document Setup: Record all technical configurations, workflows, and logic for future audits. 4. Testing - Evaluate Performance: Run performance checks based on predefined metrics. - Perform Integration Tests: Ensure seamless data flow between the agent and other systems. - Conduct User Experience Tests: Validate that the agent offers an intuitive experience to users. - Test Edge Cases: Simulate rare or extreme scenarios to check the agent’s robustness. 5. Deployment - Launch Agent: Release the AI agent into the production environment. - Make Sure Guardrails are Working: Confirm the guardrails trigger and respond correctly. - Observability: Set up monitoring, logging, and tracing pipelines to track agent behavior. - Compliance Validation: Reaffirm that the deployed system complies with all legal and organizational policies. 6. Maintenance - Monitor Agent Objectives: Regularly check if the agent is still aligned with its original purpose. - Optimize Operations: Optimize operations as per need. - Act Upon User Feedback: Use feedback for iterative improvements. If you want to see these stages in action, I would highly recommend joining our cohort for enterprise teams to build scalable agentic systems, 🔗 Enroll here: https://lnkd.in/gA3zhcfm The book includes all the basic knowledge you need to learn AI Agents as well as our 5-level Agent progression framework for business leaders. 🔗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

🔥 Can We Build Inclusive Agentic Systems Without Inclusive Training Data? When I first heard people talk about agentic AI — machines that can reason, decide, and act on our behalf — I was fascinated. But then one question hit me hard: How can we expect inclusive intelligence from exclusive data? I see this every day. The more I use these systems, the clearer it becomes — they speak one cultural language fluently, and stumble on the rest. The logic feels Western. The tone feels corporate. The empathy feels… selective. Let’s break down why that matters: → Most training data still comes from English-speaking, digitally rich nations. → The behaviors encoded reflect a small slice of humanity. → The missing perspectives aren’t “edge cases” — they’re billions of people. Now imagine giving that system agency. A biased chatbot can misinform. A biased agent can act — negotiate, reject, decide — without ever seeing the full picture of humanity it represents. So what’s the way forward? In my opinion, we can’t fix this with PR statements or prompt engineering — we need infrastructure-level inclusion: ✅ Build decentralized data pipelines where local communities own their voice and context. ✅ Incentivize global annotation networks that reflect cultural nuance. ✅ Create regulatory sandboxes for testing fairness dynamically, not statistically. ✅ And most importantly — give non-English regions a stake in how foundational models evolve. Because inclusion isn’t a “nice to have.” It’s an engineering challenge. If we don’t solve it now, agentic AI won’t just replicate bias — it’ll automate it. So I’ll ask again: can an AI truly act for everyone if it was only ever trained to understand some? #AIethics #AgenticAI #BiasInAI #Inclusion #ResponsibleAI #FutureofAI

Still the Hottest Open Source project for Agents this year: MCP from Anthropic, and everyone should pay attention to it. The 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (MCP) is set to become the new standard for connecting AI Agents & Assistants to the systems where data resides, including content repositories, business tools, and development environments. Its goal is to enable AI systems to produce more relevant and context-aware responses. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: While AI models continue to advance in reasoning and quality, their capabilities are often constrained by limited access to data. Each new data source requires a custom integration, making truly connected systems difficult to scale. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: MCP provides a universal, open standard to securely connect AI tools with data sources. Instead of fragmented integrations, a single protocol simplifies development and ensures scalability, allowing AI systems to access the data they need efficiently. 𝗪𝗵𝗮𝘁 𝗠𝗖𝗣 𝗢𝗳𝗳𝗲𝗿𝘀: - A Specification and SDKs: Enabling developers to quickly implement MCP in their systems. - Local MCP Server Support: Integrated with Claude Desktop apps for seamless use. - An Open-Source Repository: Pre-built MCP servers for popular systems like Google Drive, Slack, GitHub, and Postgres. By standardizing the connection between AI systems and data sources, MCP enables AI tools to maintain context as they interact across different systems. This approach replaces fragmented integrations with a sustainable, scalable architecture. Learn more about the project and get started today here: https://lnkd.in/gi4BREN4 Github link to the MCP Servers repo: https://lnkd.in/gmYCvhST

"this toolkit shows you how to identify, monitor and mitigate the ‘hidden’ behavioural and organisational risks associated with AI roll-outs. These are the unintended consequences that can arise from how well-intentioned people, teams and organisations interact with AI solutions. Who is this toolkit for? This toolkit is designed for individuals and teams responsible for implementing AI tools and services within organisations and those involved in AI governance. It is intended to be used once you have identified a clear business need for an AI tool and want to ensure that your tool is set up for success. If an AI solution has already been implemented within your organisation, you can use this toolkit to assess risks posed and design a holistic risk management approach. You can use the Mitigating Hidden AI Risks Toolkit to: • Assess the barriers your target users and organisation may experience to using your tool safely and responsibly • Pre-empt the behavioural and organisational risks that could emerge from scaling your AI tools • Develop robust risk management approaches and mitigation strategies to support users, teams and organisations to use your tool safely and responsibly • Design effective AI safety training programmes for your users • Monitor and evaluate the effectiveness of your risk mitigations to ensure you not only minimise risk, but maximise the positive impact of your tool for your organisation" A very practical guide to behavioural considerations in managing risk by Dr Moira Nicolson and others at the UK Cabinet Office, which builds on the MIT AI Risk Repository.

In an unprecedented and concerted effort to shape the legal and ethical landscape of AI, a tsunami of AI standards are currently in various stages of development. These standards, spearheaded by different ISO/IEC Joint Technical Committee working groups, are set to clarify key terminologies, shape system requirements, and guide users in implementing AI technologies effectively and responsibly. Some notable standards include ISO/IEC CD 5339 and ISO/IEC 25059:2023, which respectively offer guidelines for AI applications and a quality model for AI systems. ISO/IEC DTS 25058 and ISO/IEC CD TR 24030 provide crucial guidance for evaluating the quality of AI systems and present a diverse range of AI use cases. Data quality is a major focus, with a family of ISO/IEC DIS 5259 standards dealing with addressing data quality for analytics and machine learning, including data quality management requirements, the data quality process framework, and data quality governance. AI transparency is tackled by ISO/IEC AWI 12792, while issues of unwanted bias in machine learning tasks are addressed by ISO/IEC CD TS 12791. ISO/IEC CD TS 8200 further ensures the controllability of automated AI systems. In the domain of ethical and societal concerns, ISO/IEC TR 24368:2022 and ISO/IEC TR 24030:2021 provide overviews of ethical and societal considerations and use cases for AI respectively. Meanwhile, standards like ISO/IEC 23894:2023 offer guidelines for risk management of AI applications. Compliance with these ISO/IEC standards could play a role in assisting companies to align with the new AI regulations like the forthcoming EU AI Act. The AI Act will regulate high-risk AI systems, mandate transparency, fairness, robustness, and human oversight, among other requirements. Standards such as ISO/IEC AWI 12792 and ISO/IEC CD TS 12791, which cover AI transparency and unwanted bias, could provide companies with guidelines on meeting the EU's requirement for transparency and non-discrimination. Demonstrable compliance could then serve as evidence in the case of any legal disputes relating to these aspects. Likewise, the ISO/IEC 23894:2023 standard, which offers guidelines for risk management of AI applications, aligns with the AI Act's emphasis on safety and risk management. Adherence to this standard could potentially provide a framework for demonstrating compliance of these regulatory obligations. Data quality management is another area where the ISO/IEC DIS 5259 standards could assist in ensuring adherence to the AI Act. The Act requires that high-risk AI systems are trained, validated, and tested with good quality datasets, and compliance with these ISO/IEC standards could help companies fulfill this requirement. However, it's important to stress that while these standards can guide and support compliance, they are not a replacement for comprehensive legal advice tailored to the specifics of a company's situation and jurisdiction.

🚀 HUGE NEWS: Microsoft just announced support for Google's A2A protocol in Azure AI Foundry- As someone who's worked at both Google and Microsoft, seeing them collaborate on open standards makes my heart sing! 𝐖𝐡𝐚𝐭'𝐬 𝐀2𝐀? Think of it as a universal translator for AI agents. Instead of custom integrations between platforms, agents can now "speak" the same language and collaborate seamlessly. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:  ✅ Innovation unlocked - Developers can focus on building value, not reinventing communication wheels ✅ Enterprise flexibility - Mix and match best-of-breed AI agents without vendor lock-in ✅ Leveled playing field - Smaller players can compete and integrate more easily ✅ Market growth - The AI agent market is set to explode from $7.8B to $52B+ by 2030 Imagine your Microsoft scheduling agent coordinating perfectly with a Google email agent. That future? It's arriving fast. 𝐖𝐡𝐚𝐭'𝐬 𝐍𝐞𝐱𝐭? 📈 Developers: Get ready for standardized multi-agent systems 🏢 Businesses: Start planning agent networks for complex workflows 🔒 Everyone: Keep security top-of-mind as we build these distributed systems This isn't just about two companies agreeing on a standard - it's about building the foundation for truly collaborative AI. As someone who champions open standards, I'm incredibly optimistic about where this leads. Want to dive deeper? Check out the A2A GitHub repository and start experimenting. The age of collaborative AI is here! What are your thoughts on AI agent interoperability? How do you see this impacting your work? 👇 #AI #MachineLearning #OpenStandards #Innovation #Microsoft #Google #A2A #AIAgents #TechLeadership #Collaboration

If you have been wondering why did we need MCP in the first place, let me give you a detailed breakdown of why, and how AI engineers can leverage it. As AI tools grow more powerful, one big limitation has held us back: models aren’t useful unless they can take action in the real world. They need access to tools, data, and systems, whether that’s your file system, calendar, GitHub, Slack, or database. Until recently, we used function calling to wire these tools to LLMs. But as use cases evolved, function calling started to crack under pressure. What was broken with function calling? ❌ Developers had to handwrite JSON schemas and glue code for each function, even across similar tools. ❌ Models could invoke powerful actions with minimal user oversight or approval paths. ❌ No standard format or API. Each vendor had its own logic. No interoperability. Reuse was hard. ❌ No shared context. Every tool call was stateless- no history, no memory, no continuity. Tada, hence "MCP" was built. MCP is a open standard pioneered by Anthropic that makes LLMs context-aware and action-ready. It turns your AI assistant into a secure, modular system that can reason, act, and communicate with the world around it, safely. How AI Engineers Can Use MCP (You can connect your models to 👇 ): 📂 Document tools (e.g., read, summarize, and extract from files) 🧠 Dev tools (e.g., analyze code changes, open PRs, file issues) 🗓 Productivity tools (e.g., draft emails, schedule meetings) 📣 Communication tools (e.g., post to Slack, log tasks in Notion) All using a standardized, context-rich protocol. And it’s model-agnostic, so you’re not locked into one provider. 🧰 Here’s how MCP works: 1. Host: The user-facing entry point, like Claude Desktop, Cursor, or your own AI app, where prompts are entered and responses rendered. 2. MCP Client: A lightweight middleware inside the host that translates prompts into structured API calls. Think of it as the traffic router, directing requests to the right subsystem. 3. MCP Servers: Containerized or standalone services that expose specific tools, e.g., one talks to your file system, another to Slack or GitHub, each using a consistent protocol schema. 4. Tools: Functions the model can call, like read_file, send_slack_message, or query_database. Think of them like REST or gRPC endpoints. 5. Resources: The actual data the model acts on, docs, PRs, events, tickets, stored locally or accessed remotely. MCP enables safe, context-aware interaction with them. So, if you're building agentic AI systems or AI-native apps, understanding MCP is becoming table stakes. PS: If you want to go deeper into how you can use MCP in your applications, I highly recommend that you checkout this upcoming webinar on 7th May by Reid Robinson, Tal Peretz, and Matt Brown. It’s a free webinar and you will get a recording too. Link in comments 👇 ♻️ Share this with your network to spread knowledge :)

AI is following a familiar arc in technology evolution—starting with proprietary breakthroughs, moving through standardization, followed by commoditization, and finally landing where every major tech shift ultimately does: at the core of integration, data, and now, domain intelligence. In the end, everything becomes a data and context problem—requiring orchestration of models, systems, and domain knowledge to solve complex business workflows and real-world challenges effectively. A Proven Pattern: How Tech Evolves: History shows a consistent transformation cycle across every major technology wave: 🔹 Innovation – New capabilities emerge, often in proprietary silos 🔹 Standardization – Open frameworks enable rapid and widespread adoption 🔹 Commoditization – Accessibility rises, and the value shifts away from exclusivity 🔹 Integration, Data & Domain Intelligence – True differentiation moves to orchestrating systems, mastering proprietary data, and embedding domain expertise 📌 Examples: ◾ Linux & Apache – From proprietary systems to open standards powering modern infrastructure ◾ Java & Middleware – Creating a common language for enterprise-scale applications ◾ Kubernetes & Cloud Native – Commoditizing cloud orchestration, pushing competition to operational integration AI’s Transition: From Proprietary to Open : AI is now shifting from proprietary control to open innovation—with powerful open-source models like QwQ-32B, Mistral, Llama 3, and Falcon driving democratization. ✅ Standardization – Open tools and frameworks simplify AI adoption ✅ Commoditization – Broad access reduces exclusivity and shifts focus to differentiated capabilities ✅ Integration, Data & Domain Intelligence – Organizations that integrate AI deeply with their data and industry-specific knowledge will lead Agentic AI: The Next Frontier 🚀 : Agentic AI marks the next phase—combining open-source LLMs with reasoning and orchestration frameworks to drive intelligent autonomy. These systems can: 🔹 Select Models Intelligently – Dynamically choose the right model for the context 🔹 Reason Autonomously – Move beyond predictions to structured, goal-driven decisions 🔹 Orchestrate Holistically – Integrate multiple models with workflows, data sources, and domain-specific logic 🔹 Ensure Data Privacy – Enable full control over sensitive information with privacy-by-design architecture 🔹 Deploy Locally – Run models and pipelines on secure, on-prem or edge environments—without reliance on external APIs Just as Kubernetes redefined infrastructure, open Agentic AI frameworks will redefine enterprise intelligence. 🏁 The Real Competitive Advantage ✅ The future belongs to those who can orchestrate models, systems, and domain knowledge—not just to deliver accurate outcomes, but to do so efficiently, responsibly, and at scale—with cost, performance, and sustainability in mind.