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💡“AI That Writes Your Test Cases!” Did you know that AI can now automatically generate complete software test cases? By using machine learning and historical data analysis, it can create thousands of scenarios that testers used to build manually. The result? Massive time and effort savings, broader coverage, and faster product releases with higher quality. The future is already here — the question is, are your tools ready for it? 👇 What do you think — would you trust AI-generated test cases entirely? #ArtificialIntelligence #AITesting #SoftwareTesting #QualityAssurance #MachineLearning #TestAutomation #DigitalTransformation #TechInnovation #FutureOfQA #LinkedInTech

🎓 Excited to share my evolution from an AI enthusiast to an AI builder! With my recent certification, I’ve upgraded my tools & roadmaps for designing LLM automations and agentic workflows that align strategy, execution with measurable productivity & ROI. 🧠 Key insights: • Build reusable, optimized agents with clear instructions, context, & guidelines • Turn multistep workflows into automated systems using triggers, nodes, & connected systems • Implement scalable AI governance with brand voice control, analytics, & ROI tracking • Accelerate prototyping through vibe coding, validate ideas, and operationalize proven concepts using Model Context Protocol (MCP) servers 💼 Why it matters: For enterprises, this means better ROI, lower costs, & safer AI adoption. The real advantage lies in creating systems that think, learn, & scale alongside your business. What does this mean? Here are 3 ways I can help you: 1️⃣ AI Strategy & Systems Design I help leaders and teams integrate AI into real workflows from automations and assistants to scalable governance frameworks. Let’s design systems that think, learn, and deliver measurable impact. 2️⃣ Digital Transformation & Productivity Consulting I partner with organizations to modernize how they work by optimizing performance, data flow, and decision-making using practical AI tools and process design. 3️⃣ Speaking & Content Strategy Book me for keynotes, panels, or team workshops on the future of AI, digital transformation, and human-centered innovation. Let’s turn complex technology into clear, actionable insights. If you’re exploring how to implement AI automations into your workflows or organization, let’s connect. #AIstrategy #DigitalTransformation #FutureOfWork #AIproductivity #AgenticAI

For many AI leaders we talk with, Claude has become the tool of choice for safe, reasoning-driven AI. But moving from “let’s try it” to enterprise-grade deployment is where the real challenge starts -> data security, context integration, ROI, and scale. At deepsense.ai, we work with teams who already use Claude - or plan to - helping them turn experiments into production systems. From MCP integration that securely connects Claude to internal APIs, to agentic workflows automating research or compliance tasks, we focus on what matters: building solutions that work inside the enterprise, not just in demos. Whether it’s 👉 modernizing legacy systems with Claude Code, building secure 👉 internal assistants, or deploying multi-step business 👉 agents, our goal is simple - help you move faster, safer, and with measurable value. If you’re exploring what Anthropic's Claude can really do in your organization, learn more here: 👉 https://lnkd.in/dUB5y57b

How #intelligentagents navigate complex tasks seamlessly? There’s an #AIframework guiding the every move and once you know it, will surprise you - 𝐓𝐡𝐞 𝐒𝐏𝐀𝐑 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤. • 𝐒𝐞𝐧𝐬𝐞 Like your eyes scanning the street before crossing, agents must first perceive their environment. They gather signals from prompts, web data, documents, or MCP Servers. Without this awareness, no action can begin. • 𝐏𝐥𝐚𝐧 Just as you plan your route by checking maps and traffic, agents use reasoning methods such as Chain-of-Thought (CoT), Graph-of-Thought (GoT), or Reflexion. This step defines the smartest sequence of actions. Frameworks like CUA Agents (Manus, OpenAI Operator) showcase this step in action. • 𝐀𝐜𝐭 Planning is useless without execution. Agents move into action - calling APIs, generating reports, updating systems, or scheduling tasks. This is where ideas convert to reality. • 𝐑𝐞𝐟𝐥𝐞𝐜𝐭 After action comes review - the secret phase often overlooked. Agents assess feedback, learn from outcomes, and refine their future approach. Like your sprint retrospectives, this boosts long-term success. Why does it matter? Because every powerful agent you interact with today follows these four silent steps. Understanding SPAR gives you a lens into designing smarter, more reliable AI solutions. #𝐒𝐏𝐀𝐑𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤

🧠 Outstanding work by Shubham Saboo and the Google team — this 50-page Introduction to AI Agents guide is one of the most complete, real-world resources I’ve seen on building deployable agentic systems, not just demos. ⚙️ What I love most is the distinction between prototype-level agents and production-grade architectures. The document captures exactly what many of us face in practice — hallucinations, unpredictability, and scaling challenges — and translates those into design principles for stability, governance, and orchestration. 🧩 The “Think → Act → Observe → Repeat” loop is at the heart of every robust agentic system. Combine that with Shubham’s 5 Levels of Agents (from tool-connected to self-evolving systems), and you get a roadmap for how intelligent automation will mature in 2025 and beyond. 🚀 The separation of Model (brain), Tools (hands), and Orchestration (nervous system) aligns perfectly with modern Hybrid RAG + MCP architectures — where structured reasoning, controlled execution, and contextual memory drive safety and scale. 👏 Huge respect to Shubham and contributors like Antonio Gulli and Alan Blount for open-sourcing knowledge that every AI builder, PM, and researcher can learn from. This isn’t just a guide — it’s a framework for the next generation of intelligent agents. #AgenticAI #AIEngineering #AIProductDesign #RAG #MCP #AIOrchestration #LLM #AIAgents #GoogleAI #Kaggle #AIArchitecture #ResponsibleAI #GenerativeAI

Graph-based Agent Planning It lets AI agents run multiple tools in parallel to accelerate task completion. Uses graphs to map tool dependencies + RL to learn the best execution order. RL also helps with scheduling strategies and planning. Major speedup for complex tasks.

Ever wondered how AI systems actually talk to all those databases, tools, and files behind the scenes? That’s where things usually get messy in every model, every source, every integration has its own setup. Storm MCP is trying to fix that. It’s an open, enterprise-grade gateway that uses Anthropic’s Model Context Protocol (MCP) to connect LLMs directly with RAG data sources, tools, and even file systems all through one standardized interface. What I find interesting is how it makes complex things simple like context sharing, tool invocation, and file management so developers can focus on building useful AI features instead of managing endless integrations. Plus, it’s open source, scalable, and designed for enterprise-level performance. If you’re exploring how to make your AI workflows cleaner, faster, and easier to manage this might be worth checking out. 👉 Check out here: https://tryit.cc/cHEOq7T

Agentic Behavior Levels. Not all AI apps are built equal. Only those that drives enough value thrieve in the market. Here is a framework for analizing AI Software: 1. Tool integration ≠ intelligence An AI with 50 API connections (Level 1) often delivers less ROI than one that strategically engineers context and learns from feedback 2. The ROI sweet spot is Level 2 Multi-agent systems (Level 3) sound impressive, but they're still constrained by LLM reasoning limits. The real returns come from single agents that automate expertise, not just tasks. 3. Value comes from role transformation The highest ROI isn't faster execution—it's democratizing capabilities that previously required entire teams. Thinking in first principles helps cut through the hype. Full framework breakdown at Medium. https://lnkd.in/evfj-mrD

𝐘𝐨𝐮𝐫 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤. 𝐖𝐢𝐥𝐥 𝐢𝐭 𝐰𝐨𝐫𝐤 𝐰𝐡𝐞𝐧 𝟏,𝟎𝟎𝟎 𝐮𝐬𝐞𝐫𝐬 𝐡𝐢𝐭 𝐢𝐭 𝐚𝐭 𝐨𝐧𝐜𝐞?  Here's how to make sure: 𝟏. 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫:  Understand the problem you’re solving.  Who will use your agent?  What are the main goals (speed, accuracy)?  Also, consider privacy or bias risks at this stage. 𝟐. 𝐃𝐞𝐬𝐢𝐠𝐧:  Plan how your AI agent will behave.  Define rules, decisions, and logic (ReAct or Plan-Do).  Choose if it will be a chatbot or a multi-agent system. 𝟑. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭:  Make your agent smarter by connecting it to the right tools and APIs.  Add memory so it can learn from previous chats.  Use LangChain to integrate everything. 𝟒. 𝐏𝐫𝐨𝐦𝐩𝐭:  Teach your agent how to respond to people by creating clear prompts and examples.  Break down information so it can understand and react better. 𝟓. 𝐆𝐫𝐨𝐮𝐧𝐝:   Give your agent real data to help it think clearly.  Break complex info into smaller parts and use tools like HuggingFace to make it smarter. 𝟔. 𝐓𝐞𝐬𝐭:  Make sure your agent works as expected.  Test it with real-world examples and fix any problems you find.  Keep improving it based on feedback. 𝟕. 𝐃𝐞𝐩𝐥𝐨𝐲:   Launch your agent live.  Use tools like FastAPI or Docker to run it, track performance with LangSmith, and monitor errors to ensure it keeps running smoothly. Building an AI agent involves more than just coding—it’s about design, learning, testing, and refining. By following these 7 stages, you can build an AI agent that’s ready for real-world use. How are you approaching your AI agent development? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/esF52fm5

A well-structured breakdown by Anurag (Anu) on how to build scalable and production-ready AI agents. 👏 I particularly like how this framework emphasizes not just building an AI agent, but engineering it for reliability — from problem discovery and prompt design to real-world deployment and monitoring. As someone deeply interested in AI-driven transformation and practical solution delivery, I find this approach both systematic and actionable. 💡 My takeaway: Scalability and grounding in real data are what transform an AI prototype into a production-grade agent. 🔗 Original post by Anurag (Anu) — a must-read for anyone serious about building robust AI agents. #AI #AIAgents #LangChain #AIEngineering #DigitalTransformation #MachineLearning