How AI, onions, and ogres relate to layers

🚀 Building Responsible Generative AI Solutions Generative AI is no longer just an experiment — it’s moving into enterprise-grade solutions. But success goes beyond building models; it’s about the entire lifecycle: 🔹 Solution Development – starts with identifying the right business use cases that actually solve pain points, not just “shiny AI projects.” It involves selecting suitable foundation models, designing scalable data and ML architectures, and ensuring security and compliance are built in from the start. 🔹 Application Development – building intuitive, user-centric applications powered by GenAI. This means integrating LLMs with enterprise APIs and databases, applying prompt engineering, fine-tuning models where needed, and ensuring the application aligns with existing business workflows. 🔹 Evaluation & Governance – GenAI cannot be a “black box.” This stage focuses on continuous testing of model accuracy, bias detection, ethical alignment, and compliance with regulatory requirements. Strong governance frameworks help define approval workflows, access control, and auditability for every output. 🔹 Deployment & Monitoring – moving beyond the proof of concept into production at scale. This requires DevOps/MLOps pipelines, robust monitoring of latency, token costs, and model drift, along with feedback loops for retraining and fine-tuning. Security, scalability, and responsible use remain top priorities. ✨ The journey doesn’t end at deployment — it requires constant iteration, human-in-the-loop oversight, and alignment with evolving compliance and customer needs. 🌍 With AI adoption accelerating across industries, organizations that embed responsible practices early will be the ones to unlock long-term value and trust. 👉 Generative AI is powerful, but only when governed and deployed with responsibility. 💡 How is your team approaching evaluation and governance of GenAI applications? #GenerativeAI #AI #MachineLearning #ResponsibleAI #DataEngineering #Innovation #AIinBusiness

🌊 From Products to Systems: The Agentic AI Shift is a Wave, Not a Mountain! If you’re in tech, you can feel it — the ground is shaking. We’re not just building apps anymore; we’re orchestrating autonomous, goal-pursuing systems — what we now call Agentic AI. This isn’t just about faster code. It’s challenging decades of software engineering orthodoxy. Imagine staring at what you thought were mountains, only to realize they’re massive waves bearing down on you. The question isn't whether to ride the wave, but how to harness it. 🔑 Key Takeaways: 🌀 The “Product” is now a System Fixed features are dissolving into adaptable, autonomous agents that can perceive, reason, plan, and act. 👉 Why it matters: Competitive edge shifts from sleek UIs to effective, safe, and adaptive workflows. 🧭 Human Judgment is the Differentiator Agents handle the how — speed, automation, scale. Humans define the what and why — strategy, ethics, empathy. 👉 Why it matters: Your judgment becomes the non-commoditized moat. 🔒 Governance Builds Trust As agents gain autonomy, oversight and transparency aren’t optional — they’re existential. 👉 Why it matters: Without guardrails, audit trails, and human-in-the-loop controls, agents turn from asset to risk. 🎯 3 Actionable Tips for Leaders: 👤 CIO → Establish a Unified Data Fabric + Governance 📌 Agentic AI is only as good as its data. Break down silos, enforce freshness, and codify ethical rules from day one. 👤 Director of Software Engineering → Architect with Auditing + Controls 📌 Treat agents as a service layer. Bake in observability, mandatory human checkpoints, and continuous feedback loops. 👤 Senior Product Manager → Redefine “Product” as Goal-Pursuing Workflows 📌 Move from backlog grooming to outcome-definition. Frame problems as multi-step goals (e.g., incident triage, market research) for agents to own end-to-end. The hard problems persist: understanding people, aligning to business value, and designing within complex systems. But here’s the shift — your job is no longer doing it all. It’s to encode that human insight so autonomous systems can act on it, at scale. 📖 Full article below #AgenticAI

Harness built AI that writes deployment pipelines. But the CEO won't let it deploy to production. The reason reveals everything about AI's real limits. Jyoti Bansal's approach is brilliant. Harness AI creates the pipeline. Humans audit and approve. This isn't about distrust. It's about smart automation. The numbers tell the story: - Software teams spend only 30-40% of time coding - The rest goes to testing, securing, and deploying - 100 enterprise customers already use Harness AI in production Bansal's insight: "Engineers need AI taskmaster skills." What does this mean? 🔍 Review AI-generated code carefully 🔄 Iterate on AI solutions 📝 Audit every deployment pipeline The future isn't AI replacing humans. It's AI handling the tedious work while humans make the critical decisions. Harness breaks tasks into smaller components. Multiple AI agents cross-verify each other's work. The system learns from past builds and security tests. Result? Better accuracy. Fewer hallucinations. More confidence. The key principle: separate generation from verification. Use different AI systems for each step. Just like separating accounting from auditing. Smart organizations will embrace this hybrid approach. AI for speed and scale. Humans for judgment and accountability. How are you balancing AI automation with human oversight in your DevOps workflows? #DevOps #AI #Automation𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gFiZ4YwY

Harness built AI that writes deployment pipelines. But the CEO won't let it deploy to production. The reason reveals everything about AI's real limits. Jyoti Bansal's approach is brilliant. Harness AI creates the pipeline. Humans audit and approve. This isn't about distrust. It's about smart automation. The numbers tell the story: - Software teams spend only 30-40% of time coding - The rest goes to testing, securing, and deploying - 100 enterprise customers already use Harness AI in production Bansal's insight: "Engineers need AI taskmaster skills." What does this mean? 🔍 Review AI-generated code carefully 🔄 Iterate on AI solutions 📝 Audit every deployment pipeline The future isn't AI replacing humans. It's AI handling the tedious work while humans make the critical decisions. Harness breaks tasks into smaller components. Multiple AI agents cross-verify each other's work. The system learns from past builds and security tests. Result? Better accuracy. Fewer hallucinations. More confidence. The key principle: separate generation from verification. Use different AI systems for each step. Just like separating accounting from auditing. Smart organizations will embrace this hybrid approach. AI for speed and scale. Humans for judgment and accountability. How are you balancing AI automation with human oversight in your DevOps workflows? #DevOps #AI #Automation𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gPpaxXPM

Mistral AI introduces AI Studio, a platform for enterprises to build, evaluate, and manage AI workflows with governance and traceability. Currently in private beta, it provides tools for on-premise, VPC, and hybrid AI deployment environments. #mistral

NEW ARTICLE: Types of AI Agents: Complete Guide 2025 AI agents are transforming from theoretical concepts to practical business assets in 2025. Understanding the different types of agents is crucial for any organization looking to leverage automation effectively. Agents operate on a spectrum of intelligence, from simple reflex agents that follow basic if-then rules to advanced learning agents that improve their performance over time through experience. The key distinction lies between individual AI agents and agentic AI systems that orchestrate multiple agents to solve complex problems. Businesses are already seeing significant ROI from utility-based agents that aim for optimal outcomes rather than just any outcome. Case studies show a 22% increase in average order value through hyper-personalization and a 40% reduction in production bugs through autonomous DevOps systems. The future lies in collaborative architectures where hierarchical agents act as managers delegating tasks and multi-agent systems work as decentralized swarms to tackle massive challenges. These systems are proving incredibly resilient and efficient in dynamic environments like logistics and financial markets. Understanding the PEAS framework (Perception, Environment, Actuators, Sensors) provides the foundation for defining any agent's capabilities and matching the right agent type to specific business challenges. Join The AI Revolution at https://lnkd.in/d4CVujyQ

The Hidden Cost of AI: When Innovation Becomes an Expensive Demo In the rush to integrate AI into every product and service, we're witnessing a concerning trend: AI is becoming an expensive demo with features, especially for those who build based on others' tech stack and infrastructure. The Dependency Trap Many companies are racing to add "AI-powered" to their product descriptions, but at what cost? When your innovation is built entirely on third-party infrastructure, whether it's OpenAI's API, cloud providers' AI services, or other proprietary platforms, you're not building sustainable technology. You're building a demo that runs on someone else's expensive meter. The Real Costs Beyond the API Bill The financial burden extends far beyond monthly API costs: Vendor lock-in: Your entire product roadmap becomes dependent on external pricing decisions Margin compression: As AI providers adjust their pricing, your profit margins evaporate Limited differentiation: When everyone uses the same underlying technology, features become commoditized Scalability concerns: Success becomes a problem when your costs scale linearly with usage The "Features" Illusion What appears as innovative features are often just creative wrappers around existing AI capabilities. The question we should ask: Are we building genuine value, or are we creating elaborate demonstrations of what's already possible? A Path Forward This isn't an argument against AI adoption. It's a call for strategic thinking: Understand your dependencies: Know exactly what you control versus what you rent Build proprietary value: Focus on domain expertise, data, and processes that differentiate you Calculate true costs: Factor in all dependencies, not just current API pricing Plan for independence: Consider open-source alternatives and custom models for core features Prioritize sustainable architecture: Design systems that can evolve beyond current vendor relationships The Bottom Line AI is transforming industries, but sustainable businesses aren't built on expensive demos. They're built on solid foundations, clear value propositions, and technology strategies that serve the business, not the other way around. Before adding that next AI feature, ask yourself: Am I building lasting value, or am I building an expensive dependency? What's your experience with AI integration costs? Have you found ways to balance innovation with sustainability? Share your thoughts in the comments.

"DX was the core piece we needed to make data-driven decisions around AI tooling.” - Snehal Shinde, Director of Engineering, Mercari Mercari made a conscious decision to become an AI native organization, DX helped them realize that goal in a measured, realistic manner. We aren't looking to fuel an unattainable AI hype cycle with ludicrous goals, we're looking to get leaders the information they need to make targeted, smart investments into AI tooling where it's needed. https://lnkd.in/gx749ffQ

🚀 The AI frontier is shifting Enterprises no longer settle for AI that only answers questions. The next leap is AI that can act with intent: orchestrating tasks, automating processes, and delivering real business impact. This is the promise of Agentic AI: ✔ From decision-support to autonomous execution ✔ From experiments to production-ready transformation ✔ From hype to measurable value But success doesn’t come from adopting the most advanced architecture. It comes from choosing the right level of autonomy with intent: workflows, single agents, or multi-agent systems, aligned to real business outcomes. Agentic AI is about empowering people, transforming processes, and creating a future where enterprises are smarter, more resilient, adaptive, and human-centered. 👉 Read the first part of our series on Agentic AI here: #AgenticAI #Automation #DigitalTransformation

5 Leading Agentic AI Frameworks for 2025 What is Agentic AI? AI that can act autonomously to achieve specific goals without constant human guidance. It's a step beyond traditional AI, which primarily focuses on processing information or automating predefined tasks. We're talking about AI agents that plan, execute, and collaborate on complex tasks autonomously. Here are the top frameworks working in production right now: 1️⃣ CrewAI - CrewAI facilitates cooperation among AI agents for coordinated task completion using organized planning and task distribution. The fact that over 600,000 CrewAI Agents have been created in just a week is WILD. 2️⃣ LangChain - Chains AI components into sophisticated workflows. Supports memory, tool integration, and structured reasoning. Essential for building RAG systems and automation pipelines. 3️⃣ Microsoft AutoGen - Event-driven orchestration for multiple AI agents. Built for cloud automation and enterprise IT operations. Modular architecture ensures seamless scalability. 4️⃣ LangGraph - Graph-based approach for managing AI workflows with synchronized dependencies. Ideal for supply chain optimization and sequential processing tasks. 5️⃣ Semantic Kernel - Integrates AI reasoning directly into enterprise applications. Combines memory, skill orchestration, and plugins for intelligent search and decision-making. Companies using these frameworks are automating 80% of their operational workflows while others are still debating AI's potential impact. The change is already here. The only question is: are you reinventing your current architecture patterns? Over to you: Which framework matches your current automation challenges?