Integrating AI In Engineering Solutions

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.

Want to build scalable, serverless generative AI apps in a practical way? 🙌 I’ve found the perfect GitHub repo for you! Clare Liguori (Senior Principal Engineer, Amazon Web Services (AWS)) shares examples using AWS Step Functions and Amazon Bedrock to orchestrate complex AI workflows with techniques like: ✨ Prompt chaining to break down tasks into sequential prompts  👯♀️ Parallel execution to run multiple prompts simultaneously ✅ Conditional logic and human approval steps  ...and more! The "Plan a Meal" demo is clever - watch two AI chef agents debate and iteratively improve recipe ideas based on provided ingredients. 🍝 An AI then writes the full recipe for the winning meal concept! For developers excited about generative AI's potential but unsure how to actually build production apps, this repo is a must-see. No need to start from scratch! 💡 You can leverage your existing AWS service expertise with patterns you already know and love, gradually blending AI capabilities into your skillset. I wrote up a blog post guide that dives deeper into the examples 🔗 https://lnkd.in/eHhNSEWT Whether for analysis, writing, planning, or exploring new use cases, this resource makes serverless generative AI much more accessible. Have you built anything cool combining serverless and AI? Share your creations below! 👇 📌 Save + Share! 👩🏻💻 Follow Brooke Jamieson to learn about Generative AI and AWS Tags 🏷 #AWS #CloudComputing #Serverless #GenerativeAI #PromptChaining #AmazonBedrock #AWSStepFunctions #LargeLanguageModels #Developers

Imagine smarter robots for your business. New research from Google puts advanced Gemini AI directly into robots, which can now understand complex instructions, perform intricate physical tasks with dexterity (like assembly) and adapt to new objects or situations in real time. The paper introduces "Gemini Robotics," a family of AI models based on Google's Gemini 2.0, designed specifically for robotics. They present Vision-Language-Action (VLA) models capable of direct robot control, performing complex, dexterous manipulation tasks smoothly and reactively. The models demonstrate generalization to unseen objects and environments and can follow open-vocabulary instructions. It also introduces "Gemini Robotics-ER" for enhanced embodied reasoning (spatial/temporal understanding, detection, prediction), bridging the gap between large multimodal models and physical robot interaction. Here's why this matters: At scale, this will unlock more flexible, intelligent automation for the future of manufacturing, logistics, warehousing, and more, potentially boosting efficiency and enabling tasks previously too complex for robots as we've imagined in the past. Very, very promising! (Link in the comments.)

"Industrial IoT Middleware for Edge and Cloud: The OT/IT Bridge with Apache Kafka and Flink" => Modernization of industrial IoT integration and the shift toward cloud-native architectures. As industries embrace digital transformation, bridging Operational Technology (OT) and Information Technology (IT) has become crucial. The OT/IT Bridge plays a vital role in industrial automation by ensuring seamless data flowbetween real-time operational processes and enterprise IT systems. This integration is fundamental to the Industrial Internet of Things (#IIoT), enabling industries to monitor, control, and optimize their operations through real-time data synchronization while improving Overall Equipment Effectiveness (#OEE). By leveraging Industrial IoT middleware and data streaming technologies like #ApacheKafka and #ApacheFlink, businesses can establish a unified data infrastructure, enabling predictive maintenance, operational efficiency, and smarter decision-making. Explore a real-world implementation showcasing how an edge-to-cloud OT/IT bridge can be successfully deployed: https://lnkd.in/eGKgPrMe

A new paper from Technical University of Munich and Universitat Politècnica de Catalunya Barcelona explores the architecture of autonomous LLM agents, emphasizing that these systems are more than just large language models integrated into workflows. Here are the key insights:- 1. Agents ≠ Workflows Most current systems simply chain prompts or call tools. True agents plan, perceive, remember, and act, dynamically re-planning when challenges arise. 2. Perception Vision-language models (VLMs) and multimodal LLMs (MM-LLMs) act as the 'eyes and ears', merging images, text, and structured data to interpret environments such as GUIs or robotics spaces. 3. Reasoning Techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, and  Decompose, Plan in Parallel, and Merge (DPPM) allow agents to decompose tasks, reflect, and even engage in self-argumentation before taking action. 4. Memory Retrieval-Augmented Generation (RAG) supports long-term recall, while context-aware short-term memory maintains task coherence, akin to cognitive persistence, essential for genuine autonomy. 5. Execution This final step connects thought to action through multimodal control of tools, APIs, GUIs, and robotic interfaces. The takeaway? LLM agents represent cognitive architectures rather than mere chatbots. Each subsystem, perception, reasoning, memory, and action, must function together to achieve closed-loop autonomy. For those working in this field, this paper titled 'Fundamentals of Building Autonomous LLM Agents' is an interesting reading:- https://lnkd.in/dmBaXz9u #AI #AgenticAI #LLMAgents #CognitiveArchitecture #GenerativeAI #ArtificialIntelligence

Google DeepMind has introduced new AI models, 𝐆𝐞𝐦𝐢𝐧𝐢 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐆𝐞𝐦𝐢𝐧𝐢 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬-𝐄𝐑, aimed at improving robots’ ability to adapt to complex real-world environments. These models leverage large language models to enhance reasoning and dexterity, enabling robots to perform tasks such as folding origami, organizing desks, and even playing basketball. The company is collaborating with start-up Apptronik to develop 𝐡𝐮𝐦𝐚𝐧𝐨𝐢𝐝 𝐫𝐨𝐛𝐨𝐭𝐬 using this technology. The advancements come amid competition from Tesla, OpenAI, and others to create AI-powered robotics that could revolutionize industries like manufacturing and healthcare. 𝐍𝐯𝐢𝐝𝐢𝐚’𝐬 𝐂𝐄𝐎, 𝐉𝐞𝐧𝐬𝐞𝐧 𝐇𝐮𝐚𝐧𝐠, 𝐡𝐚𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐫𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚 𝐦𝐮𝐥𝐭𝐢𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧-𝐝𝐨𝐥𝐥𝐚𝐫 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲. Unlike traditional robots that require manual coding for each action, Gemini Robotics allows robots to adjust to new environments, follow verbal instructions, and manipulate objects more effectively. The AI runs in the cloud, leveraging Google’s vast computational resources. Experts praise the development but note that general-purpose robots are still not ready for widespread adoption. 𝐑𝐞𝐚𝐝 𝐌𝐨𝐫𝐞: https://lnkd.in/gd4gAtFp

🔎 Many industrial operators face the same challenge: "How can we use AI to detect anomalies early enough to prevent unplanned downtime?" That’s a question I often hear in conversations with customers. During a recent visit with Daniel Mantler, our product manager for edge computing, he shared a use case that addresses exactly this challenge. As we all know by now, AI is no longer rocket science. But getting it into real life industrial applications still seeems to be. And that's where our team of experts developed a lean and fast to adapt setup that uses local sensor data to detect for example vibration, temperature, or anomalies directly at the machine. A lightweight machine learning model runs on an edge device and identifies deviations from normal behavior in real time. Because the data is processed on-site, latency is minimal and data sovereignty is maintained. Both aspects are critical in many industrial environments. But the real value lies in the practical benefits for operators: Faster reaction times, reduced dependency on external infrastructure, and the ability to integrate AI into existing systems without needing a team of data scientists. What are your thoughts on integrating ML into edge architectures? I’m keen to hear your thoughts. Let’s use the comments to share perspectives and learn from one another. For those who want to dive deeper into the technical setup and learnings, here’s the full article: 🔗 https://lnkd.in/e8Z5HMCH #artificialintelligence #machinelearning #edgecomputing

The Rise of Industrial AI: What it is and Why it Matters Consumer AI personalizes daily life, enhancing convenience and effortless creation. Industrial AI goes deeper—reengineering core processes that power economies, transforming productivity, safety, and environmental sustainability. MIT defines Industrial AI as the application of AI to improve, automate, and optimize large-scale industrial processes, in sectors like manufacturing, aerospace, oil and gas, and utilities. At its core, #IndustrialAI uses machine learning, predictive analytics, and data processing to optimize complex industrial environments in real-time, enabling systems to anticipate issues—whether by foreseeing equipment malfunctions or adjusting supply chains dynamically. In the next 3-5 years, Industrial AI will shift from enhancing efficiency to becoming indispensable — whether for automating factories or managing assets through "digital twins" (virtual replicas of physical assets) for unprecedented control and precision. Integrating Industrial AI with emerging fields like quantum computing, will also open doors to complex problem-solving previously deemed insurmountable. How Will Industrial AI Transform Key Sectors? · Aerospace & Defense: boost safety, fleet efficiency through predictive maintenance and analytics. · Manufacturing: drive smart factories with automated workflows, reducing waste and raising productivity. · Telecoms: optimize network reliability and performance as 5G and IoT demands surge. · Oil & Gas: enhance operational safety and environmental compliance through predictive monitoring. · Utilities: strengthen grid resilience and energy efficiency by predicting demand and integrating renewables. · Engineering & Service: extend asset longevity and reduce costs with AI-driven maintenance and real-time insights. Implications for Government and Policy: Governments will fund and prioritize #AI initiatives to stay competitive. As Industrial AI becomes critical to sectors like energy, defense, telecoms etc, countries will need robust data privacy and cybersecurity to mitigate risks associated with its integration into essential and sensitive sectors. Labor displacement accompanies any industrial revolution. High-skill jobs will emerge in AI management, while automation in repetitive tasks will mean retraining policies and ethical AI deployment becomes paramount. Developing nations with strong industrial bases may accelerate economically through AI-driven efficiency, while economies slower to adopt Industrial AI risk falling behind. Industrial AI also supports #sustainability goals, optimizing energy consumption, reducing waste, and enabling efficient resource allocation. This shift promises not only economic benefits but also environmental gains, enhancing urban infrastructure and quality of life.

Why Hardware-Software Co-Design Is Non-Negotiable? Dangerous assumption: Design them independently, then stitched together later. From my experience building scalable, field-tested industrial IoT solutions, I can confidently say this approach is flawed—and costly with cause of many failures in industrial deployments. Whether you're monitoring pressure in oil & gas pipelines or automating maintenance in a smart city infrastructure, the reliability, scalability, and total cost of ownership of an IoT system depend deeply on how well the hardware and software are integrated—side by side—from day one. Technical Reasons 1. Power efficiency and performance Battery-operated devices, especially in LPWAN and NB IoT environments, require tightly optimized firmware that aligns with hardware capabilities (sleep modes, sensor wake cycles, transmission windows, and many other factors). Designing software without a deep understanding of the hardware's physical and firmware limitations results in shorter lifespans, inconsistent data, or both. 2. Connectivity optimization Protocols like LoRaWAN, NB-IoT, or Cat-M1 are not just plug-and-play. Reliable transmission depends on antenna design, shielding, payload formatting, and retry mechanisms that must be embedded in both hardware specs and software logic—together. 3. Real-time fault detection and recovery Industrial environments are noisy—electrically, physically, and digitally. Integrating diagnostics, fallback strategies, and sensor validation into both firmware and cloud platform ensures that small glitches don’t turn into expensive field failures. 4. OTA updates and lifecycle management Without co-design, firmware updates become a logistical nightmare. A unified design ensures that remote updates are reliable, secure, and hardware-aware—so they don't brick your devices in the field. Non-Technical (But Just as Critical) Reasons 1. Lower long-term cost Reworking firmware or cloud APIs post-production is exponentially more expensive than doing it right upfront. Co-design reduces iteration cycles, deployment delays, and support overhead. 2. Faster time to market When teams work in silos, integration becomes a bottleneck. Side-by-side development removes surprises and streamlines validation—cutting months off your release timeline. 3. Better user experience From installation to data visualization, a co-designed solution feels cohesive. Installers don’t struggle with mismatched instructions. Platform users don’t question sensor data accuracy. Everyone wins. 4. Future-proofing the solution When hardware and software evolve in sync, scaling to new features or integrating with third-party platforms becomes a natural progression—not a painful migration. So, be assured hardware and software designed in the same room, by teams who speak the same language? If not, you're probably not building a solution. You're building a future problem. Let’s build smarter. #lpwan #IoT #lorawan #nbiot #ellenex

𝗧𝗵𝗲 𝗜𝗜𝗼𝗧 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸: 𝗔𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗟𝗲𝗻𝘀 𝗼𝗳 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 Standards are the foundational "language rules" of #IIoT. While classic #Fieldbus and supervisory protocols have historically facilitated communication at the device and plant levels, newer standards bridge interactions with #cloud-based business systems. 𝗠𝗤𝗧𝗧 𝗮𝗻𝗱 𝗦𝗽𝗮𝗿𝗸𝗽𝗹𝘂𝗴 𝗕: 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 The lightweight #MQTT protocol, originally conceived for bandwidth-limited and unstable network conditions, has become a go-to solution for IIoT connectivity. It uses a Pub/Sub model that only sends data during event changes, reducing network congestion and cutting data transfer costs. Its strong quality-of-service (QoS) levels ensure message delivery in harsh network conditions, an ideal feature for industrial environments. #SparkplugB builds on MQTT, introducing consistent data structures and payloads that allow for real-time data monitoring and device tracking. Its hierarchical topic namespaces improve data organization, facilitating data management across several industrial systems. 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀: 𝗠𝗼𝘃𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗣𝘂𝗿𝗱𝘂𝗲 𝗠𝗼𝗱𝗲𝗹 The layered Purdue model, which is traditionally used in industrial systems, finds challenges when adapting to the volume, variety, and velocity of Industrial Internet of Things (IIoT) data. New architectures are emerging to address these limitations: ▪ 𝗛𝘂𝗯-𝗮𝗻𝗱-𝗦𝗽𝗼𝗸𝗲: This model centralizes data publication through hubs, such as MQTT brokers, before distributing it to multiple applications, consolidating data, and enriching it with contextual metadata. Multiple consumers can access it without overwhelming individual systems. ▪ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 (𝗨𝗡𝗦): #UNS is structured through hierarchical topic organization, organizing access to IIoT data. This approach is based on standards like #ISA-95, logically categorizing data to simplify its discovery and usability. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗗𝗮𝘁𝗮𝗢𝗽𝘀 𝗮𝗻𝗱 𝗔𝗜 #DataOps is a discipline that promotes a data-centric culture, breaking down #IT and #OT silos, establishing data governance frameworks for clear data ownership and access, ensuring accessibility, consistency, and usability, and aligning business and technical teams with data-driven objectives. Through data contextualization, where data is tailored to specific use cases, #AI improves data quality, automates system data mapping, and turns it into actionable intelligence. Source: https://t.ly/VPT9C ***** ▪ Follow me and ring the 🔔 to stay current on #IndustrialAutomation, #IndustrialSoftware, #SmartManufacturing, and #Industry40 Tech Trends & Market Insights!