Scalable System Design

IBM's CEO says AI could replace 30% of back-office roles in five years. Your LinkedIn feed is drowning in AI transformation posts. But here's what I learned studying how The Coca-Cola Company and DHL actually implemented AI: speed without infrastructure is chaos. Here's exactly how to build AI capability without becoming obsolete: 1. Start with a six-person sandbox (not 50,000-person panic) Coca-Cola sent zero employees to AI bootcamp. Instead, they picked six people in legal, comms, and tech. They built a sandbox and tested DALL-E and GPT for months before touching anything critical. Result: the Create Real Magic campaign where customers remixed Coke imagery with AI. The campaign itself barely mattered. What mattered was learning what worked before risking real operations. Pratik Thakar, their head of generative AI: "We created a sandbox." Your move - but start smaller. Even two people part-time beats zero people full-time. Test on internal processes first. 2. Use DHL's two-door system for every AI decision DHL's voicebot handles 1 million calls monthly. Resolves half without humans. But it started by failing to recognize "Ja" (German for yes). They could have panicked. Instead, they created this framework: - Two-way doors are reversible. Test email triage with LLMs. Try new pricing algorithms in one region. Move fast. - One-way doors are permanent. AI in safety-critical manufacturing. Customer-facing automation in regulated markets. Move slow. This language killed analysis paralysis. Everyone knows when to sprint, when to crawl. 3. Fix your Frustration Zone problem first Most companies sit in high AI urgency, low readiness. Symptoms: consultants everywhere, 80% have major data gaps, demos look amazing but production fails. Moving faster makes it worse. DHL spent six months getting work councils aligned before scaling. Built Gaia, their closed AI hub. Tested customs-coding helpers. Coca-Cola took six months on their sandbox before any public deployment. Yes, six months feels long when the board wants results yesterday. But broken AI at scale takes years to fix. 4. Identify who will own your AI infrastructure layer Your AI transformation needs champions who bridge potential and reality. People who run the first two-way door experiments. Document what breaks (everything will). Know which decisions are reversible. Build the sandboxes others will use. These champions become your most valuable players. They own the infrastructure layer while everyone else chases trends. Find them. Protect them. Let them build slowly. Companies winning with AI move deliberately, not desperately. They build foundations. Others chase demos. Start with sandboxes. Skip the slideware. What are you building this quarter? P.S. Want the full AI infrastructure playbook? See my complete article in the first comment.

The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-agent workflows) provide the backbone for orchestrating complex tasks. ADK and LlamaIndex help stitch together knowledge and actions. 𝟮. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Agents don’t operate in isolation. They must plug into the real world:  • Third-party APIs for search, code, databases.  • OpenAI Functions & Tool Calling for structured execution.  • MCP (Model Context Protocol) for chaining tools consistently. 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Memory is what turns a chatbot into an evolving agent.  • Short-term memory: Zep, MemGPT.  • Long-term memory: Vector DBs (Pinecone, Weaviate), Letta.  • Hybrid memory: Combined recall + contextual reasoning.  • This ensures agents “remember” past interactions while scaling across sessions. 𝟰. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Raw LLM outputs aren’t enough. Reasoning structures enable planning and self-correction:  • ReAct (reason + act)  • Reflexion (self-feedback)  • Plan-and-Solve / Tree of Thought These frameworks help agents adapt to dynamic tasks instead of producing static responses. 𝟱. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 Scalable agents need a grounding knowledge system:  • Vector DBs: Pinecone, Weaviate.  • Knowledge Graphs: Neo4j.  • Hybrid search models that blend semantic retrieval with structured reasoning. 𝟲. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 This is the “operations layer” of an agent:  • Task control, retries, async ops.  • Latency optimization and parallel execution.  • Scaling and monitoring with platforms like Helicone. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 No enterprise system is complete without observability:  • Langfuse, Helicone for token tracking, error monitoring, and usage analytics.  • Permissions, filters, and compliance to meet enterprise-grade requirements. 𝟴. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Agents must meet users where they work:  • Interfaces: Chat UI, Slack, dashboards.  • Cloud-native deployment: Docker + Kubernetes for resilience and scalability. Takeaway: Scaling AI agents is not about picking the “best LLM.” It’s about assembling the right stack of frameworks, memory, governance, and deployment pipelines—each acting as a building block in a larger system. As enterprises adopt agentic AI, the winners will be those who build with scalability in mind from day one. Question for you: When you think about scaling AI agents in your org, which area feels like the hardest gap—Memory Systems, Governance, or Execution Engines?

GenAI is easy to start but hard to scale. Too many companies are stuck in endless pilots. Here’s what it takes to build GenAI capability. McKinsey has recently published their findings from working with 150+ companies on their GenAI programs over two years. Two hurdles stand out: 𝟭. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗲: Teams waste time on duplicate experiments, wait on compliance processes, and solve problems that don’t matter. 30% - 50% of innovation time is spent trying to meet compliance - not building. 𝟮. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲: Even when a prototype works, most companies can’t get it into production. Risk, security, and cost barriers overwhelm teams, leading to stalled or cancelled deployments. According to McKinsey the most successful GenAI platforms contains three core components: 𝟭. 𝗔 𝘀𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: To support both innovation and scale, companies need a secure, centralized portal that gives teams easy access to pre-approved gen AI tools, services, and documentation. It should enable developers to quickly build with reusable patterns, while also offering governance features like observability, cost controls, and access management. The best portals promote contribution and reuse across the organization, reducing friction and accelerating development at scale. 𝟮.𝗔𝗻 𝗼𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗿𝗲𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Scaling GenAI requires modular, open architecture that enables teams to reuse services, application patterns, and data products across use cases. Leading companies build libraries of common components (like RAG, embeddings, or chat workflows) and focus on integration via APIs - not vendor lock-in. Infrastructure and policy as code ensure changes can propagate quickly and securely across the platform, reducing cost and accelerating deployment. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: To scale safely, GenAI platforms must embed automated governance that enforces compliance, manages risk, and tracks costs. This includes microservices that audit prompts, detect policy violations (like sharing sensitive personal data or generating inaccurate responses), and attribute usage to specific teams. A centralized AI gateway enforces access controls, logs interactions, and routes traffic through security filters - allowing flexibility where needed. These guardrails accelerate approval processes, reduce setup time, and let teams focus on building value - not managing risk manually. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? Source: McKinsey & Company 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

The biggest risk in AI isn't missing the wave - it's drowning in it. 8 critical questions that separate successful AI implementations from costly failures: In 2023, rushed AI partnerships led to devastating consequences: data breaches, compliance violations, and reputational damage. I've watched brilliant CTOs choose AI orchestration platforms based on flashy demos, only to see their infrastructure crumble 6 months later. AI orchestration is your operation's central nervous system. One misconfiguration can trigger system-wide failures. The real threats are silent killers: • Data drift corrupting model accuracy • Security vulnerabilities exposing sensitive data • Compliance violations triggering massive fines Here are the questions the top 1% of tech leaders ask: 1. Integration Capabilities Don't just check basic tech stack support. Dive into: • API versioning strategies • Legacy system integration approaches • Hybrid cloud deployment capabilities 2. Security & Compliance Demand evidence of: • Data residency controls • Privacy sanitizers for PII removal • Security incident history • Automated compliance monitoring 3. True Total Cost of Ownership Look beyond licensing fees: • Model serving costs • Training & tuning expenses • Operational support requirements • Infrastructure upgrade needs 4. Continuous Monitoring Your platform must provide: • Quality evaluations • Hallucination detection • Automated retraining triggers • Real-time performance tracking 5. Scalability Architecture Get specifics about: • Maximum concurrent deployments • Resource allocation mechanisms • Load balancing strategies • Failover protocols 6. Model Governance Ensure robust: • Data locality & PII protection • Privacy data sanitization • Decision audit trails • Explainability tools 7. Efficiency Framework Evaluate: • Value-to-effort ratio • System reliability metrics • Resource optimization • Operational consistency 8. Implementation Track Record Request: • Detailed case studies • Reference calls • Documentation of past failures • Proof of successful scaling At CrewAI, we're helping Fortune 500 companies transform operations with AI agents, orchestrating thousands of automated workflows daily. Want to learn more about enterprise AI orchestration? Let's connect. #AI #Technology #Innovation #Leadership #EnterpriseAI

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible. This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management. Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture : 1.🔸Decomposition Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator). 2.🔸Communication Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration. Support both single-agent and multi-agent setups for small or distributed workflows. 3.🔸Deployment Deploy agents in containerized or serverless environments using Docker or Modal. Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows. 4.🔸Data & Discovery Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access. Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning. 5.🔸Testing & Observability Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions. Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate. 6.🔸UI & Style Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes. Enable collaborative, proactive, and goal-driven reasoning among multiple agents. 7.🔸Security Protect access with token-based authorization and data encryption. Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution. 8.🔸Cross-Cutting Concerns Handle configuration, secrets, and environment management. Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design. Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns. Save this post to understand the architecture that powers the next generation of AI systems #AgenticAI

GitHub used to have a monolithic database handling 950,000 txn/sec 🤯 But this database became a pain as they scaled. So, a natural fix was to break this database into smaller ones, but this turned out to be more complex than they thought. I just published a video dissecting their entire process and the exact steps they took to do it without any downtime while making sure none of the existing functionalities break. Give it a watch - youtu.be/Tq1fif3rcnQ Going through this video, you will understand how databases are migrated without downtime, how to approach such a complex problem one step at a time, and you will get a practical look into real-world database scaling challenges and solutions.

🚀 What if your LLM got faster the more you used it? - That is the promise of self-adaptive speculative decoding! Left = Slow LLM, Middle = Fast LLM, Right = Faster LLM adapting to your prompts over time! Red slow tokens are from 670B param DeepSeek, blue/black fast ones are from a 8B small LLM trained to copy the big model! Speculative decoding lets a small model, a speculator, “guess ahead” what a large model will say, and the big model only needs to verify, not regenerate, every token. The result: 2-3× faster inference for large LLMs. But there’s been a catch: traditional speculators are static, fixed after training, unable to adapt when workloads or domains shift. This means that over time they get worse at guessing the larger model tokens as data distributions shift. The promise behind Together AI’s new AdapTive-LeArning Speculator (ATLAS) System is that the small model can learn and adapt to your prompts so that it’s correct more often and can speed the larger model up even if the questions you're asking your model change over time. 🧠How does it work? ATLAS introduces: 🔹 A static speculator trained on broad data for stability and fallback. 🔹 A lightweight adaptive speculator that continuously fine-tunes on real-time traffic, learning from new inputs as they arrive. 🔹 A confidence-aware controller that dynamically balances accuracy and speed by adjusting speculation lookahead. 📈 The results? 🔹 Up to 400% faster decoding (from 105 to 501 tokens/sec). 🔹 Sustained gains during RL training, cutting rollout time by >60%. 🔹 Real-time specialization to each user’s evolving input patterns. In short: the more you use it, the faster it gets! 🚀 Full Blog below 👇

AI Agents have moved from simple scripts to multi-agent systems Understanding the stages helps save time and cost... Multiple reports suggest the issue isn’t AI workflows themselves, but how people design them. Often, you don’t need a complex agentic system for simple tasks like summarizing HR documents. That’s why it’s crucial to pick the right solution instead of chasing the biggest one. 📌 To make it clearer, let’s walk through the 5 stages: 1. Script Chatbots - Human Dependency (~90–80%): Almost fully dependent on humans to script every single response. - Autonomy: No real intelligence — purely rule-based workflows. - Scalability: Scales linearly, but only for repetitive, predictable tasks. - Use Case: Simple automations like email replies, FAQs, or support ticket routing. 2. LLM Chatbots - Human Dependency (~70–60%): Reduced, but still needs supervision. - Autonomy: Contextual understanding with natural conversations — but no planning ability. - Scalability: Expands easily for large customer support operations. - Use Case: Customer-facing chatbots that can hold human-like conversations, but can’t take autonomous action. 3. Modern RPA - Human Dependency (~50–40%): Handles repeated, structured tasks with less manual input. - Autonomy: Contextual but still repetitive — can trigger scripts and execute tools when prompted. Scalability: Great for high-volume, process-driven workflows. - Use Case: Hiring document processing, invoice scanning, compliance checks. 4. Single Agentic AI - Human Dependency (~30–20%): Agents plan, use tools, and incorporate feedback with limited supervision. - Autonomy: Adaptive reasoning within a defined scope — memory + planning + tool use. - Scalability: Dynamic scaling for dedicated enterprise use-cases. - Use Case: Smart document retrieval, enterprise knowledge Q&A, semi-autonomous research. 5. Multi-Agentic AI - Human Dependency (~15–10%): Agents coordinate among themselves, requiring minimal human input. - Autonomy: Dynamic, multi-workflow execution with cross-agent collaboration. - Scalability: Designed for complex, large-scale enterprise automation. - Use Case: Interconnected coding agents, enterprise-wide orchestration, cross-department AI systems. In our latest book, we explored what each of these stages means for enterprises, not just in theory. I’ve linked the detailed breakdown in the comments 👇 📌 The big takeaway: Those reports are right, which state that the problem is not models or workflows - it is people who are implementing them. Not every task needs a complex system — sometimes simpler approaches are more effective. That’s why identifying the right use case is critical for enterprises. And that’s exactly what we cover in our AI Agent Engineering course — helping you design scalable agents with the right enterprise mindset. 🔗 Enroll here: https://lnkd.in/gA3zhcfm Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

Most people evaluate LLMs by just benchmarks. But in production, the real question is- how well do they perform? When you’re running inference at scale, these are the 3 performance metrics that matter most: 1️⃣ Latency How fast does the model respond after receiving a prompt? There are two kinds to care about: → First-token latency: Time to start generating a response → End-to-end latency: Time to generate the full response Latency directly impacts UX for chat, speed for agentic workflows, and runtime cost for batch jobs. Even small delays add up fast at scale. 2️⃣ Context Window How much information can the model remember- both from the prompt and prior turns? This affects long-form summarization, RAG, and agent memory. Models range from: → GPT-3.5 / LLaMA 2: 4k–8k tokens → GPT-4 / Claude 2: 32k–200k tokens → GPT-OSS-120B: 131k tokens Larger context enables richer workflows but comes with tradeoffs: slower inference and higher compute cost. Use compression techniques like attention sink or sliding windows to get more out of your context window. 3️⃣ Throughput How many tokens or requests can the model handle per second? This is key when you’re serving thousands of requests or processing large document batches. Higher throughput = faster completion and lower cost. How to optimize based on your use case: → Real-time chat or tool use → prioritize low latency → Long documents or RAG → prioritize large context window → Agentic workflows → find a balance between latency and context → Async or high-volume processing → prioritize high throughput My 2 cents 🤌 → Choose in-region, lightweight models for lower latency → Use 32k+ context models only when necessary → Mix long-context models with fast first-token latency for agents → Optimize batch size and decoding strategy to maximize throughput Don’t just pick a model based on benchmarks. Pick the right tradeoffs for your workload. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Contrary to popular belief, having a GTM team offsite will not fix your go-to-market problem. Neither will a pipeline meeting on Wednesdays. Neither will a CMO-CRO bi-weekly coffee meeting. Neither will firing your CMO and trying to hire a unicorn marketing leader. It’s a Band-Aid. It might make it easier for people to work together. It might patch up the problem for a while that will come back to you in 3 months when you’re missing your pipeline for Q4. It’s a Band-Aid. The real solution? Redesign your GTM (aka the Factory that produces your revenue) - Starting with Financial Planning, Modeling, and Budgeting, and then working across the rest of GTM team to Sales, Marketing, Sales Dev, Ops, Post-Sale, etc. 1. Build a Unified View of GTM with Financial Data & GTM Data that measures both performance (effectiveness) and unit economics (efficiency) 2. Align the entire GTM leadership team on a core KPI stack that has *nothing* to do with attribution by department or channel 3. Categorize and evaluate GTM investment portfolio allocation by customer lifecycle stage, NOT DEPARTMENT. 4. Methodically break down compound metrics to isolate the biggest issues / risks / opportunities by customer lifecycle stage 5. Build and align on cross-functional initiatives to solve the biggest issues in your Revenue Factory 6. Monitor and evaluate impact against the core KPI stack that has nothing to do with attribution by department or channel. #finance #gtm #b2b #sales #marketing p.s. Just to drive home the message - you should be able to *clearly* understand how your GTM is performing and isolate the biggest issues/opportunities without ever discussing or using attribution by channel or department 🙂