Demo vs. Production Robotics in the Workplace

Many AI agents look impressive in demos, but crash in real-world production. Why? Because scaling agents requires engineering discipline, not just clever prompts. Moving from prototype to production means tackling memory, observability, scalability, and resilience challenges. Let’s explore the design principles that make AI agents production-ready. 🔸Why AI Agents Fail Monolithic designs, missing scalability, and poor observability often break agents under real-world traffic. 🔸Microservices Architecture Break agents into services like inference, planning, memory, and tools for flexibility and fault tolerance. 🔸Containerization & Orchestration Use containers for packaging and Kubernetes for orchestration. Make it a habit from prototype to multi-agent production. 🔸Message Queues & Async Processing Prevent bottlenecks with task queues, event sourcing, and non-blocking communication. 🔸Continuous Delivery (CI/CD) Automate deployments with a three-stage pipeline for faster, safer updates. 🔸Load Balancing for Real Traffic Distribute 50–5,000+ requests/minute with API gateways, application layers, and service mesh. 🔸Scalable Memory Layer Use Redis for short-term context, SQL/NoSQL for structure, and Vector DBs for knowledge. 🔸Observability & Monitoring Log calls, monitor latency, and enable human-in-the-loop reviews for deeper debugging. The real test for AI agents goes beyond a demo to survive production traffic at scale. Have you had this experience? #AIAgent

This is why so many demos never make it to production. Getting AI demos to work under controlled conditions is simple. Getting AI products to scale and support real-world operations or customers is completely different. An autonomous delivery drone with a 99.9% reliability isn’t as good as it sounds because it crashes every 1,000 trips. Trying to scale that up from a successful demo of 10 deliveries to doing 10 deliveries an hour for a week reveals the problem. In the digital paradigm, building the small circle solves most of the problems you’ll encounter building the biggest circle. In the data and AI paradigms, building the small circle teaches you very little about building the biggest one. Every data and AI minimum viable product scales on two axes: functionality and reliability. Meeting functional thresholds is always easier than meeting reliability requirements. The costs of small reliability improvements can be massive. The only way to learn how to build data and AI that scales is to build it for scale from the start. Building an AI demo doesn’t prove that the solution is viable or that scaling is feasible. It’s critical to build data and AI products iteratively, but we must change the way the business thinks about those iterations.

🚨 Reality Check: 95% of AI agents die in the "demo to production" valley of death. You've seen it: • Agent works perfectly in demo ✅ • Founders get excited 📈 • Months later... still "almost ready" 😵 👉 Why agents fail to scale: ❌ No observability into decisions ❌ Security vulnerabilities everywhere ❌ Memory that forgets context ❌ Can't integrate with real systems 🎯 A must have resource Nir Diamant just dropped "Agents Towards Production" - the most comprehensive open-source playbook for GenAI agents that actually scale. 🔗 GitHub: NirDiamant/agents-towards-production (8.9K+ stars) What's different: 📚 Tutorial-first: Every concept = runnable code 🛡️ Production security patterns 🧠 Memory systems that work at scale 🛠️ End-to-end observability 🚀 Real deployment strategies 💡 Why This Matters The demo is 10% of the work. Production is the other 90%. This repository tackles every production challenge: • Agent hallucinations at scale • Memory architecture for long conversations • Debugging agent decisions • Security patterns that actually work With code, not just theory. Hot take: Companies using this playbook ship agents 6-12 months faster. What's your biggest agent production challenge? 👇 🎍 Kaddo Nir Diamant

We had an interesting challenge this week that I think many organisations will face increasingly often. We built a demonstration to show what's possible with AI. A "show, don't tell" approach to prove that AI could solve a specific business problem. It worked brilliantly. Perhaps too brilliantly. The client was so impressed they wanted to put it straight into production immediately. The problem was, it was built on spreadsheets that had been hacked together just to prove a point. We had zero robustness in naming, mapping, or any of the foundational elements. It proved the concept perfectly and would probably work fine for a single person running some planning on a weekly basis. But it definitely should never see the light of production. It wasn't built the right way, wouldn't perform at scale, and certainly wouldn't qualify for any security or compliance standards. This debate is going to come up more and more: when are you building something as a one-off throwaway that's perfectly fine for personal use, versus when do you need robustness, scalability, security, and compliance? It's the age-old challenge of shadow IT that enterprises have struggled with forever. Spreadsheets under desks, little servers running wherever, people running their businesses on local spreadsheets. But AI amplifies this massively. The platforms are becoming so capable that the breadth of what you can quickly build is extraordinary. Yet the gap between "works as a demo" and "ready for enterprise production" remains as wide as ever. I'm genuinely intrigued to see how both the IT profession and business users will respond to this duality.

𝗬𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗱𝗲𝗺𝗼: 𝗙𝗹𝗮𝘄𝗹𝗲𝘀𝘀. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: 𝗗𝗲𝗮𝗱 𝗼𝗻 𝗮𝗿𝗿𝗶𝘃𝗮𝗹. We've seen this story 100+ times. Here's why it keeps happening... McKinsey's latest research on 150+ companies confirm what we've witnessed firsthand: 30-50% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗼𝗻 𝗶𝗻𝗳𝗿𝗮 𝘄𝗼𝗿𝗸 instead of innovation, and most promising prototypes never make it to production. Sound familiar? You're not alone. The Reality Check ✅ Building AI agents with SaaS tools? Easy. ❌ Building them securely for enterprise at scale? Completely different game. Most organizations hit two walls: Failure to innovate - Teams drowning in infra cycles Failure to scale - Security concerns killing promising pilots 𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁: 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗲 𝗛𝗮𝗿𝗱 𝗪𝗮𝘆 Based on our work with enterprises, we've developed a comprehensive architecture blueprint - and we learned the hard way what actually works at scale: 🔧 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 - From no-code builders to advanced API/SDK tools 🛡️ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝘆 𝗗𝗲𝘀𝗶𝗴𝗻 - Built-in guardrails, compliance automation, and governance 📊 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 - Real-time monitoring, cost management, and performance metrics 🔄 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 - Open source, closed source, and traditional ML integration ⚡ 𝗦𝗲𝗹𝗳-𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗣𝗼𝗿𝘁𝗮𝗹 - Developers get started in minutes, not weeks What Actually Works: • Automated infrastructure reduces non-essential work by 30-50% • Centralized AI gateway manages access, costs, and security policies • Reusable components accelerate development across teams • Built for scale - not just demos The difference between a cool AI demo and a production-ready enterprise solution isn't just about the model - 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘀𝘂𝗿𝗿𝗼𝘂𝗻𝗱𝘀 𝗶𝘁. Ready to move beyond pilots? Let's discuss how this blueprint can accelerate your AI transformation. 🔗 Explore the architecture: https://lnkd.in/d8QGwKqp #EnterpriseAI #AIArchitecture #MachineLearning #DigitalTransformation #AIGovernance #TechLeadership

87% of enterprise AI projects never make it past prototype. The reason isn't what anyone thinks. The problem isn't model accuracy or data quality. It's the operational complexity between demo and production that kills AI initiatives. → Compliance requirements hit: SOC-2, HIPAA, GDPR ↳ Retrofitting security adds 6-12 months to timelines ↳ Most teams underestimate this by 300-400% → Integration complexity emerges: legacy systems, APIs, data pipelines ↳ Prototype used clean test data ↳ Production faces 47 edge cases never seen in development → Observability gaps appear: debugging multi-step workflows at scale ↳ Single-agent systems become black boxes ↳ Multi-agent orchestration requires built-in tracing This is why platforms architected for production from day one reach deployment in weeks. Those built for demos spend months retrofitting or get abandoned entirely. SimplAI's multi-agentic framework includes security, integration, and observability as core architecture. That's how we go from PoC to production in approximately 1 month instead of 6-12. What's preventing your AI initiatives from moving beyond pilot programs? 🔥 Want more breakdowns like this? Follow along for insights on: → Building production-ready agentic AI systems at enterprise scale → Technical architecture decisions that determine AI implementation success PS: Whenever you're ready, take SimplAI for a spin at our website or book a demo: https://lnkd.in/g3JkSJHb

"Getting a demo working is actually the easy part. 90% of the work is after the demo." I keep saying this in enterprise conversations, and the reaction is always the same: vigorous head nodding followed by war stories. Just spoke with another Fortune 500 company. Same pattern everywhere: - Funded AI agent programs ✓ - Staffed up teams ✓ - Built impressive demos ✓ - Hit the production wall ✗ The walls are always the same: → Hallucination rates they can't trust in production → Security teams blocking deployment over authorization concerns → Token costs that destroy unit economics The most telling part? They're not planning for dozens of agents. They're planning for thousands. The gap between demo and production isn't a technology problem - it's an architecture problem. You need deterministic security around non-deterministic AI. You need user-specific permissions, not service accounts. You need authorization that happens AFTER the prompt, not before. That's what we've built at Arcade.dev. And yes, it runs in your VPC.

89% of AI prototypes never reach production. The gap? Engineering mindset. Vibe Coding gets demos running in hours. Vibe Engineering builds products that monetize. Same AI tools → Different outcomes → Massive career divergence. The Fork In The Road: Vibe Coding (prototype track): → Goal: Run now, iterate fast → Prompting: Casual, conversational → Skills needed: None (that's the point) → Output: Copy-paste code, hidden bugs → Best for: Proof of concepts, demos → Career risk: Stuck building prototypes forever Vibe Engineering (production track): → Goal: Scale safely, ship confidently → Prompting: Structured (roles, security, components, data rules, background jobs) → Skills needed: APIs, MCP connectors, DevTools, RLS/XSS basics → Output: Tested flows, monitoring logs, KPIs, alerts → Best for: Products that monetize → Career path: Own AI infrastructure, lead teams The Reality Check: Companies don't pay for demos. They pay for reliability. Your AI-generated code without review logic = production incident waiting to happen. One prompt injection → entire data breach → compliance nightmare. The Upgrade Path: Define goals + guardrails (security first) Structure your prompts + wire background jobs Connect APIs & MCP (standard protocols) Add testing, KPIs, alerts, monitoring Ship + iterate with confidence The engineers who master this transition will lead the next decade of software. The ones who don't will keep churning out prototypes while watching others build companies. Which track are you on?

🚀 The AI Production Canyon: Why There's a 10-50x Gap Between 'It Works!' and 'It Ships!' That impressive AI demo you built in a weekend? Brace yourself—it's only 2% of the journey to production. Having productionized AI systems for billions of users at Visa (ranked #2 in Fortune's AIQ among Fortune 500 companies), I can tell you this truth never gets easier to accept. At the recent AI Agent Summit at UC Berkeley, Ion Stoica (co-founder of Databricks & Anyscale) validated what those of us in the trenches know too well: "Moving from a working AI prototype to a production system requires 10x to 50x more engineering effort." Here's what that multiplier actually means: ✅ Demo (Week 1): Your LLM agent works perfectly with 5 test cases ❌ Production (Month 6): Handling 1M+ edge cases, hallucinations, and angry users at 3 AM The hidden 98% includes: • Reliability Engineering - 99.999% uptime doesn't happen by accident • Guardrails & Safety - Preventing your AI from going rogue • Observability - Understanding failures when they inevitably happen • Cost Optimization - That $100K/day API bill wasn't in the demo • Latency & Scale - Users won't wait 30 seconds for a response • Data Privacy & Compliance - GDPR, HIPAA, SOC2... the alphabet soup of requirements This aligns with Andrew Ng's MLOps principle: "The model is just the tip of the iceberg." Google's research shows that ML code is only ~5% of a real ML system (Sculley et al., 2015). My takeaway: Stop celebrating POCs. Start conquering production challenges. The hard problems live in the last mile. At Visa, we learned that real technical leadership isn't in building the demo—it's in bridging that 10-50x gap at enterprise scale. What's your biggest challenge moving AI from demo to production? 👇 #AIEngineering #MLOps #TechnicalLeadership #ProductionAI #ArtificialIntelligence #Visa

Proof-of-concepts take hours but production-grade reliability still takes months. I’ve lost count of the jaw-dropping demos I’ve seen (and built) in the last 18 months. The Gen-AI era lets us turn an idea into a working prototype before coffee gets cold. But here’s the trap: stakeholders watch that slick demo and instantly expect full-scale, 24×7, enterprise-grade performance. The gulf between the two is where products and reputations can sink. Here are three useful lessons I have learnt 1. Show the demo, but sell the definition of done. Every prototype reveal should end with a single slide titled “What Done Really Means.” List the uptime target, concurrency load, and failure budget. When stakeholders cheer, they’re cheering for that contract, not the GIF they just saw. 2. Separate demo velocity from deployment velocity. Measure prototypes in hours or days, but plan for production hardening in weeks/months. Different clocks, different KPIs, different decision gates. 3. Turn excitement into a transparent roadmap. Follow the wow-moment with a one-pager: reliability targets, scalability milestones, risk mitigations, next checkpoints. Momentum stays high, surprises stay low and everyone sees exactly how the headline demo becomes customer value. How does your team convert demo sparks into production fire? Share your tactics below.