How AI Frees Us to Focus on What Matters

🤖 𝗦𝘁𝗼𝗽 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝘁𝗯𝗼𝘁𝘀. 𝗦𝘁𝗮𝗿𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀. LLMs are powerful, but 𝘂𝗻𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 when left unchecked. What separates flashy AI demos from production-grade systems? 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀, and 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Too many teams still try to build 𝗴𝗲𝗻𝗲𝗿𝗮𝗹-𝗽𝘂𝗿𝗽𝗼𝘀𝗲 chatbots. These are 𝗳𝗿𝗮𝗴𝗶𝗹𝗲, 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁, and hard to scale. Instead: Engineer 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 agents with clear 𝗿𝗼𝗹𝗲𝘀, 𝘀𝗰𝗼𝗽𝗲𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀, and trustworthy tools. That’s how AI delivers real value in the enterprise. 𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: 🧠 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗶𝗻𝗵𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝘂𝗻𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 They’re probabilistic. That means hallucinations happen—a Stanford study showed up to 88% in legal scenarios. Without role boundaries, trust crumbles. 🔒 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗰𝗹𝗼𝘀𝗲𝗱-𝘄𝗼𝗿𝗹𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 Think insurance claims, IT tickets, or onboarding, not “write something cool.” These tasks have: 📥 Clear inputs/outputs 🧪 Testability 🧾 Explainability 🛡️ Built-in trust 🧩 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝘄𝗶𝘁𝗵 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 No AI monoliths. Instead, design 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 for 𝗮𝘁𝗼𝗺𝗶𝗰 𝘁𝗮𝘀𝗸𝘀, then orchestrate them like 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀. This is how complexity scales without chaos. 🛠️ 𝗕𝘂𝗶𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 Don’t feed LLMs raw databases. Instead, give them: 🔧 Strongly typed 📦 Constrained and scoped 📘 Self-describing 🔐 Access-controlled 🧪 𝗧𝗲𝘀𝘁 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂 𝗺𝗲𝗮𝗻 𝗶𝘁 Don’t just test final answers. Inspect how agents choose tools, reason through steps, and whether they know when to ask for help. 📡 𝗦𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗰𝗵𝗮𝘁 → 𝘀𝗶𝗴𝗻𝗮𝗹 AI shouldn’t wait for prompts, it should listen for signals (logs, tickets, drop-offs) and respond in context. 🎯 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Control isn’t a limitation. It’s what makes AI useful. Stop building “intelligent” chatbots. Start building engineered intelligence with a purpose. 🔗 Link to the article by Sean Falconer in the comments. #AI #LLM #AgenticAI #SoftwareDesign #MachineLearning

Running AI with only MLOps or only LLMOps is like trying to fly with one wing. At EliteSquad.AI, we often meet enterprises that have two powerful forces at work: Machine Learning models driving predictions, personalization, and anomaly detection. Large Language Models powering chatbots, document automation, and intelligent search. Individually, they create value. Together, without the right framework, they create chaos: Siloed teams Lack of governance Model drift and unreliable outcomes That’s why MLOps and LLMOps must go hand in hand. MLOps brings structured pipelines, monitoring, and reliable deployment. LLMOps adds governance, fine-tuning, guardrails, and responsible AI usage. At EliteSquad.ai, we help enterprises unify both worlds, so ML models and LLMs don’t just coexist, they scale responsibly and deliver real business impact. Faster deployments Lower risks AI that is powerful, trusted, and future-ready Because the future of AI isn’t about choosing between MLOps and LLMOps,it’s about making them work together.

🚀 **What “Level” of AI Adoption Are You On? Understanding the Layers of the AI World** In today’s AI-driven landscape, we often use the same buzzwords—but experience entirely different realities. Picture this: A CEO showcases "AI transformation," copying ChatGPT responses into PowerPoint and calling it business innovation. Meanwhile, a 20-year-old builds a system tracking Stripe webhooks, automatically fixing SQL tables, and only alerting him if AI confidence drops below 94%. Both claim: "We use AI in production." Both are right—but their "AI universes" couldn’t be further apart. 🏢 I see the AI world as a multi-story building, where each floor reflects a different level of AI maturity: 0️⃣ **Unaware** – AI is either hype or threat; not relevant. 1️⃣ **Novices** – Surprised that ChatGPT rewrote their resume or revived a dying cactus. Each AI update feels as groundbreaking as the first iPhone. 2️⃣ **Power Users** – Pay for premium tools; believe there’s an AI solution for every problem, even if it’s all powered by similar models. 3️⃣ **Builders** – Tired of endless copy-paste, they automate with Zapier/n8n, store prompts in git, and celebrate their “AI agents” on @["LinkedIn"](urn:li:organization:1337). 4️⃣ **Operators** – Here, monitoring, logs, and failovers matter. Automation must work at 3AM, not just as a “hobby project.” 5️⃣ **Researchers** – Read arxiv, develop custom models, publish papers—the rest are still playing in the sandbox. 📉 Each step up the ladder means fewer people: every floor is 10x smaller than the one below. You don’t “level up” from reading yet another article. True growth comes from hitting breaking points—when skepticism fades, frustration peaks, or hobby turns into must-have infrastructure. Every AI headline—such as “!!! GPT-5 is here !!!”—is perceived differently on each floor. Some ask “Is this the end?”; others, “Where’s the API?”; advanced users wonder, “How do I integrate this without breaking production?” ✨ The big takeaway? AI isn’t magic. It’s a staircase. Whether you’re just amazed by ChatGPT or debugging logs at 3AM, every stage matters. 🔎 **Which floor are you on?** Share in the comments—or read more insights here: 👉 [Full post on Telegram](https://lnkd.in/gzpVH5RW) #ArtificialIntelligence #AIAdoption #DigitalTransformation #AIForBusiness #Leadership

Last week, an AI agent went 𝗿𝗼𝗴𝘂𝗲. It 𝘣𝘺𝘱𝘢𝘴𝘴𝘦𝘥 our safeguards, 𝘪𝘨𝘯𝘰𝘳𝘦𝘥 our docs, and started making 𝗰𝗼𝘀𝘁𝗹𝘆, nonsensical API calls for hours. The 𝗰𝘂𝗹𝗽𝗿𝗶𝘁? We built our API for predictable humans, not for AI's chaotic, brilliant mind. The old "vending machine" API model is dead. Here’s how to build for the AI era: 🚀 𝟰 𝗞𝗲𝘆𝘀 𝘁𝗼 𝗔𝗜-𝗙𝗶𝗿𝘀𝘁 𝗔𝗣𝗜𝘀: 1️⃣ 💰 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮 𝗧𝗼𝗸𝗲𝗻 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 Every byte costs money. Use custId (2 tokens) over customerAccountIdentifier (4 tokens). At scale, this saves millions. 2️⃣ ⏱️ 𝗕𝗲𝗮𝘁 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗮𝘁 𝗔𝗹𝗹 𝗖𝗼𝘀𝘁𝘀 AI workflows are chains. Batch data, stream results, and predict next steps. Shaving milliseconds matters. 3️⃣ 🔄 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗲𝗹𝗳-𝗛𝗲𝗮𝗹𝗶𝗻𝗴 𝗘𝗿𝗿𝗼𝗿𝘀 Don't just say "invalid date." Teach the AI: "Error: Use YYYY-MM-DD format. Example: 2024-08-01". 4️⃣ 📚 𝗗𝗼𝗰𝘀 𝗔𝗿𝗲 𝗣𝗮𝗿𝘁 𝗼𝗳 𝗬𝗼𝘂𝗿 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 Test them, version-control them, and treat them like code. The AI will read them and learn The future is 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 APIs that adapt and collaborate in real-time. 🌌 𝗔𝗿𝗲 𝘆𝗼𝘂𝗿 𝗔𝗣𝗜𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝘆𝗲𝘀𝘁𝗲𝗿𝗱𝗮𝘆'𝘀 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝗼𝗿 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄'𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀? I'm curious: What's the #1 lesson you've learned from integrating AI? Share below! 👇 #AI #API #Developer #TechInnovation #ArtificialIntelligence #SoftwareEngineering #LLM #GPT #DevOps

This article explores how platform users access AI to solve operational bottlenecks, improve process outcomes, and unlock value across business functions.

AI agents represent the next generation of artificial intelligence. Here are 8 steps developers and engineers can take to successfully deploy AI agents. By Mandi Walls, thanks to PagerDuty

AI is moving beyond prompts. For a while, enterprises tried to make AI work through prompt engineering via typing the “right” words to get the “right” output. It was clever for demos but fragile in the real world. Prompts don’t remember, they don’t adapt, and they don’t scale. Think about it: * A customer service agent shouldn’t have to re-explain an issue every time they talk to AI. * A manager shouldn’t need to craft a “perfect prompt” just to pull last quarter’s sales numbers. * A new employee shouldn’t be fed generic onboarding when AI can tailor it to their role. What works now is different: context. When AI understands the environment it’s operating in, your knowledge base, business rules, roles, and past interactions, it stops being a one-off responder and starts acting like a true collaborator who really comes to your help. That’s where the shift unlocks; it changes how we work with technology. Conversations adapt to who you are, and workflows flow seamlessly without interruption As Nathan highlights in his recent blog, it’s not just about better answers; it’s about building AI that actually fits the way people and businesses work. Full blog here: https://lnkd.in/g_SNkyQ3 #AIagents #Compliance #RiskManagement #WealthManagement #CustomerExperience #CX #FinancialPlanning #Lending #RetailBanking #CommercialBanking #BankingOperations #BankingLeaders #BankManagement #DecisionMakers #TechnologyStrategy

Low-Code + AI isn’t just a project, it’s a capability. The question isn’t “Can we build faster?” but “Can we adapt processes every week without risk?” With platforms like #OutSystems, low-code + AI shift the bottleneck from coding to flow. Guardrails are built-in, assets get reused, and AI does the heavy lifting. The result? Faster change, fewer errors, and compounding capacity. If you gained extra capacity this quarter, would you invest it in customer experience, operations, or risk reduction? #LowCode #AI #DigitalTransformation #EnterpriseSolutions #DBResults

Anthropic's research emphasizes simplicity and composability over complex frameworks, and Spring AI implements these principles beautifully. Each agent does one thing exceptionally well, with clear interfaces and stateless design. The practical pattern: customer onboarding agent composed of three simple agents—document validator, compliance checker, and account provisioner. While each agent handles its domain expertly; the orchestrator manages the sequence. The breakthrough insight: agents can handle sophisticated tasks, but their implementation is often straightforward. Complexity belongs in coordination, not in individual agent logic. This approach scales better and debugs easier than monolithic agent systems. Source: https://lnkd.in/gJhXcdZh

A lot of attention is paid to context orchestration these days, but in the land of non-deterministic programs VALIDATION IS KING! At 99Ravens we’ve learned the hard way that if you're building an AI product you can't just hope the model will extract the right elements from your prompting and fancy context engineering, because it likely won't. We’re used to thinking of testing and QA as a step in the development process but I think that has to change. The probabilistic nature of deep neural nets and the massively diverse nature of the inputs and outputs demands a completely different approach to quality assurance. One that treats validation not as an afterthought, but as the central organizing principle of your development process and perhaps of the product itself.  To build a reliable AI product, the feedback loop from results back to prompts (user, assembly, and system level) needs to do at least these five things well: 1. Elicit Rich User Response. This means either detailed feedback or massive volumes of simple feedback. 2. Extract Relevant Elements. Your system needs to parse that feedback and identify the specific elements that matter. Was it tone? Accuracy? Format? Completeness? Without this extraction capability, you're just collecting noise and risk overfitting. 3. Assemble Alternate Paths. Based on the patterns you've identified, you need systematic ways to generate alternative approaches. This isn't just A/B testing.  Depending on the feedback it could require creating entirely different prompt strategies, context structures, or model behaviors to test. 4. Output and Compare Results. Each variation needs to be rigorously tested against the others, with clear metrics for what constitutes improvement vs. a different wrong answer. 5. Generalize the Pattern. Perhaps most critically, successful changes need to be abstracted into principles that can guide future iterations. Without this step, you're solving problems one-off rather than building systematic improvement. As AI capabilities rapidly advance, it’s easy to assume that better models will solve validation problems automatically. We've learned that (at least for now) this is wishful thinking. More powerful models actually amplify the importance of validation because they can fail in more sophisticated and subtle ways that are harder to catch. The edge cases become edgier. If you're building AI products, prepare to invest in your validation infrastructure with the same intensity you invest in your model architecture and context management.  If you’re crushing this already and have additional/other advice I would LOVE to hear about it.