💡Logs are your friends. Can’t replicate, can’t fix. So much time on Teams is lost debating what “went wrong” — when the real issue is that we can’t replicate it. At the core, most upstream reports (tech and non-tech) describe what they see rather than what can be reproduced. Understandable — people are busy and logging often isn’t deep enough to tell the full story. The solution? Comprehensive logging. If every system captured both the error and the user journey leading to it, AI could pinpoint the root cause within seconds. That requires every trace log to include: 🪪 Request ID – the unique ID for each action 🔗 Correlation ID – e.g., session ID or equivalent for stateless apps 👤 User ID – always logged, even when sessions are present ⚡ BTW — backend automated processes must also be identified by a user ID, because any job is initiated by a user, whether that user is a biological entity or a digital one. ⚡ With just the user + timestamp, you could reconstruct the full path and replicate the issue instantly — instead of wasting hours guessing what happened. Let’s make replication built in, not wishful thinking. #CantReplicateCantFix #DevOps #SoftwareEngineering #Debugging #Logging #Observability #AI #TechLeadership #Productivity

Instead of thinking of your AI tools as all-knowing agents, try thinking of them as a system of focused, specialized tools working together. In this post, our brilliant engineer Michelle Thorsell lays out a path to explore how MCP servers and sub-agents transform Anthropic's Claude from a chatbot into a junior developer that can handle the entire development flow — from ticket to tested pull request. https://lnkd.in/gYM7T7Fb #AI #Engineering #DeveloperTools #Innovation

Artificial Intelligence is transforming software development, from code generation to automated testing. The IT community is rapidly adapting, with new tools making development cycles faster and more efficient. How are you leveraging AI in your projects? #AI #SoftwareDevelopment #TechTrends

Artificial Intelligence is transforming software development, from code generation to automated testing. The IT community is rapidly adapting, with new tools making development cycles faster and more efficient. How are you leveraging AI in your projects? #AI #SoftwareDevelopment #TechTrends

The hype around LLMs is starting to feel… incomplete. 🧠 Why? Because the model itself is just the engine. *The real architectural shift is the rise of AI Agents.* We're moving beyond simple prompt-and-response interactions. The future isn't about meticulously crafting the perfect prompt. It's about defining a high-level *goal* and empowering an agent to achieve it autonomously. This requires a new stack: 🚀 *Orchestration:* A framework to break down the goal into a sequence of steps. 🛠️ *Tooling:* Giving the agent access to APIs, databases, or even other AI models to execute those steps. 💾 *Memory:* Providing short-term and long-term context so it can learn and adapt. Instead of asking an AI to "write Python code to get data from API X", you'll tell the agent: "Generate a quarterly sales performance report for the APAC region and email it to the leadership team." The agent figures out the rest. What do you see as the biggest obstacle to mainstream adoption of autonomous agents? Is it reliability, security, or the sheer complexity of the orchestration layer? #AIAgents #ArtificialIntelligence #LLM #Automation #FutureOfWork #TechTrends

Most teams rush to pick AI tools before they really know what they need them for. 🤖 And then they wonder why things don’t quite fit together. After seeing a lot of AI projects up close, I’ve realized something simple, not all tools solve the same kind of problem. Some help you build features, others help you connect everything behind the scenes. Think of it like this: 𝗧𝗼𝗽 𝗹𝗮𝘆𝗲𝗿 – where you actually build and ship things. Assistants, CI/CD, testing, monitoring. Tools like GitHub Copilot, CircleCI, or Cursor live here. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗮𝘆𝗲𝗿 – the foundation that keeps everything running. Agent frameworks, vector databases, and evaluation systems. That’s where LangChain, Pinecone, or LangSmith fit in. If your goal is to write software with AI help, focus on the top layer. If you are building AI products, you’ll need both. Choosing the right tools starts with understanding what layer you are actually working in. That’s where clarity beats complexity. Which tools have made the biggest difference for your team lately? 🤔 #AI #Innovation #SoftwareDevelopment #AIDevelopment #Automation #MSiHub

The rise of AI-assisted coding and integration for enterprise environments AI may be writing code faster — but enterprises are learning that speed without structure can create new bottlenecks. 🧩 GitLab’s latest research calls it the AI Paradox: dev teams gain efficiency through AI coding tools, yet lose productivity to scattered toolchains, compliance hurdles, and data silos. The enterprise solution? Platform engineering and model-based integration. By uniting your mobile and backend ecosystems through hubs like Planview, companies can create a single source of truth—cutting down on duplicate effort, reducing compliance risks, and enabling AI agents to work safely within enterprise-grade boundaries. It’s no longer about whether AI can code — it’s about whether your organization is architected to scale that intelligence responsibly. #EnterpriseApps #AICompliance #MobileInnovation #IntegrationStrategy #PlatformEngineering #AIMaturity

We've been lied to! We always hear: "You can't automate everything." We build slick pipelines to automate the plumbing, but we still leave the most critical part—the decision-making—to human intuition. I'm now convinced this idea is on the verge of being completely obsolete. This became crystal clear as I just wrapped up DataCamp's MLOps Fundamentals skill track. It was a fantastic journey through the entire production lifecycle, and it’s what sparked this entire thought process. Here’s the blueprint I learned: 1. The Foundation (MLOps Concepts): Understanding the full end-to-end lifecycle, maturity models, and the core architecture (feature stores, experiment tracking, containers). 2. The Code (Developing for Production): The critical mindset shift from messy research notebooks to clean, modular, and reproducible engineering. 3. The Launch (Deployment & Life Cycling): This is the "real ground." We dived into CI/CD, API deployment strategies, and, crucially, monitoring for the silent killers: data and concept drift. 4. The Brain (Fully Automated MLOps): This is where it got interesting. We built a full CI/CD/CM/CT pipeline—a loop where the system can spot its own decay and automatically trigger retraining. But here’s the "Aha!" moment. Even this "fully automated" system still needs a human to answer the real questions. "Is this drift significant, or just noise? Is this new model really better for the business, or just statistically different?" This is where the old mantra dies. With the insane, exponential rise of LLMs, why are we still using humans for this? We are on the cusp of the next leap. Forget simple automation. The future is "autonomous operation". Instead of a simple trigger, imagine deploying an AI Agent that investigates the drift, reasons about its root cause, simulates the business impact of retraining, and then makes the "go/no-go" decision. #MLOps #AI #Automation #SystemDesign #LLM #AIAgents

Source: https://lnkd.in/dUqXWCcZ 🚀 AI is reshaping software engineering—here’s how leaders can stay ahead!💡 Platform Engineering is key: Build AI-powered tools with paved roads and secure LLM integration.🔧 Composable APIs enable seamless AI agent workflows—think of them as the “glue” for modern apps.📊 AI-ready data isn’t just a buzzword—it’s the backbone of generative models. Prioritize MCP and data fabrics!🧠 Pro tip: Cultivate innovation cultures with exploration teams and psychological safety. Let engineers experiment without fear. Ready to future-proof your team? Start by rethinking your software stack today. #AI #SoftwareEngineering

Citizen developers are rewriting the automation playbook⁣ ⁣ 𝗟𝗼𝘄-𝗖𝗼𝗱𝗲/𝗡𝗼-𝗖𝗼𝗱𝗲 • 𝗔𝗜 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • 𝗘𝗱𝗴𝗲 𝗔𝗜⁣ ⁣ Traditional AI projects are slow, costly, and code-heavy — but a shift is here. Visual builders let business teams ship intelligent workflows fast.⁣ ⁣ 🔹 Highlights:⁣ - 🤖 AutoML-powered model training without deep ML skills ⁣ - ⚡ Real-time inference at the device level for instant decisions ⁣ - 🔒 Built-in security and compliance controls ⁣ - ☁️ Cloud-native scalability + visual business-rule editors⁣ ⁣ Use cases: healthcare diagnostics, fraud detection, personalized retail, predictive maintenance.⁣ ⁣ How would you apply this in your team? Share one use case below. 👇⁣ ⁣ Read more: https://lnkd.in/ec6v-2W9 ⁣ Dive in and pilot a no-code proof of value this quarter.⁣ ⁣ #DigitalRobots #WorkSmarter #IntelligentAutomation #AIforBusiness