Scalable System Design

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗜 𝘄𝗶𝘀𝗵 𝗜 𝗵𝗮𝗱 𝘄𝗵𝗲𝗻 𝗜 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀! ⬇️ Built together with Rakesh Gohel (aka Mr. AI Agent) — and now yours! We broke it down into 7 essential steps to go from zero to scalable, production-ready agents: 1. 𝗣𝗶𝗰𝗸 𝗮𝗻 𝗟𝗟𝗠 ➜ Choose a model that reasons well, supports step-by-step logic, and gives consistent outputs. Tip: Llama, Claude Opus, or Mistral are great for open-weight setups. 2. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗟𝗼𝗴𝗶𝗰 ➜ Should it reflect before answering? Plan or act directly? What if it gets stuck? Start simple with ReAct or Plan–then–Execute. Don’t overcomplicate. 3. 𝗪𝗿𝗶𝘁𝗲 𝗶𝘁𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ Define how it should respond, when to use tools, and what formats to reply in. Reusable prompt templates are your friend here — they scale better than hardcoded flows. 4. 𝗔𝗱𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 ➜ LLMs forget. Your agent can’t. Use sliding windows, summaries, or memory frameworks like MemGPT or ZepAI to persist key facts and long-term context. 5. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗔𝗣𝗜𝘀 ➜ Let the agent do things: query data, call systems, fetch information. Just be explicit about what tools exist and when to use them. 6. 𝗚𝗶𝘃𝗲 𝗶𝘁 𝗮 𝗝𝗼𝗯 Bad prompt: “Be helpful.” Good prompt: “Summarize customer feedback and suggest improvements.” Narrow scope wins. The tighter the job, the smarter the agent. 7. 𝗦𝗰𝗮𝗹𝗲 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗮𝗺𝘀 ➜One gathers data. One interprets. One formats results. You don’t need a super-agent. You need a smart team — built for specific tasks. 𝗢𝗻𝗲 𝗶𝗺𝗮𝗴𝗲. 𝗦𝗲𝘃𝗲𝗻 𝘀𝘁𝗲𝗽𝘀. 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀! From solo agents to orchestration-ready systems — this is how you scale with intent. Image below. Save it. Use it. You can find more info in the comments! (Note: The entire roadmap is not exhaustive and can differ according to different use cases) ♻️ Share this to help your network level up. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

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?

Clean code is nice. But scalable architecture? That’s what makes you irreplaceable. Early in my journey, I thought “writing clean code” was enough… Until systems scaled. Teams grew. Bugs multiplied. That’s when I discovered Design Patterns, and things started making sense. Here’s a simple breakdown that can save you hundreds of hours of confusion. 🔷 Creational Patterns: Master Object Creation These patterns handle how objects are created. Perfect when you want flexibility, reusability, and less tight coupling. 💡 Use these when: You want only one instance (Singleton) You need blueprints to build complex objects step-by-step (Builder) You want to switch object types at runtime (Factory, Abstract Factory) You want to duplicate existing objects efficiently (Prototype) 🔷 Structural Patterns: Organise the Chaos Think of this as the architecture layer. These patterns help you compose and structure code efficiently. 💡 Use these when: You’re bridging mismatched interfaces (Adapter) You want to wrap and enhance existing objects (Decorator) You need to simplify a complex system into one entry point (Facade) You’re building object trees (Composite) You want memory optimization (Flyweight) You want to control access and protection (Proxy, Bridge) 🔷 Behavioural Patterns: Handle Interactions & Responsibilities These deal with how objects interact and share responsibilities. It’s about communication, delegation, and dynamic behavior. 💡 Use these when: You want to notify multiple observers of changes (Observer) You’re navigating through collections (Iterator) You want to encapsulate operations or algorithms (Command, Strategy) You need undo/redo functionality (Memento) You need to manage state transitions (State) You’re passing tasks down a chain (Chain of Responsibility) 📌 Whether you're preparing for interviews or trying to scale your application, understanding these 3 categories is a must: 🔹 Creational → Creating Objects 🔹 Structural → Assembling Objects 🔹 Behavioral → Object Interaction & Responsibilities Mastering these gives you a mental map to write scalable, reusable, and testable code. It’s not about memorising them, it's about knowing when and why to use them. #softwareengineering #systemdesign #linkedintech #sde #connections #networking LinkedIn LinkedIn News India

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

A junior reached out to me last week. One of our APIs was collapsing under 150 requests per second. Yes — only 150. He had tried everything: * Added an in-memory cache * Scaled the K8s pods * Increased CPU and memory Nothing worked. The API still couldn’t scale beyond 150 RPS. Latency? Upwards of 1 minute. 🤯 Brain = Blown. So I rolled up my sleeves and started digging; studied the code, the query patterns, and the call graphs. Turns out, the problem wasn’t hardware. It was design. It was a bulk API processing 70 requests per call. For every request: 1. Making multiple synchronous downstream calls 2. Hitting the DB repeatedly for the same data for every request 3. Using local caches (different for each of 15 pods!) So instead of adding more pods, we redesigned the flow: 1. Reduced 350 DB calls → 5 DB calls 2. Built a common context object shared across all requests 3. Shifted reads to dedicated read replicas 4. Moved from in-memory to Redis cache (shared across pods) Results: 1. 20× higher throughput — 3K QPS 2. 60× lower latency (~60s → 0.8s) 3. 50% lower infra cost (fewer pods, better design) The insight? 1. Most scalability issues aren’t infrastructure limits; they’re architectural inefficiencies disguised as capacity problems. 2. Scaling isn’t about throwing hardware at the problem. It’s about tightening data paths, minimizing redundancy, and respecting latency budgets. Before you spin up the next node, ask yourself: Is my architecture optimized enough to earn that node?

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 🙂

"Your GTM Isn’t a Strategy—It’s a System" a $7M CEO asked me: "what’s the best go-to-market strategy for our stage of growth?" my response? "you don’t need a strategy. you need a system." most companies treat gtm like a series of disconnected tactics— 📌 launch a new outbound sequence 📌 tweak paid ads to drive pipeline 📌 invest in brand, content, or demand gen but the best b2b companies don’t run tactics. they run GTM systems. GTM is not a one-time initiative—it’s an operating system. if your growth is dependent on heroic sales reps or one-off marketing plays, you don’t have a system—you have a patchwork of tactics. if your sales and marketing teams operate in silos, you don’t have a system—you have a misalignment problem. if you’re adding pipeline but not improving efficiency, you don’t have a system—you have a leaky funnel. when GTM is a system, it runs on predictable inputs and scalable outputs. what does a gtm system look like? 1️⃣ predictable demand generation → how do we consistently create pipeline? ✅ content, brand, paid, outbound all work together (not separately) ✅ marketing & sales agree on icp, lead quality, and follow-up timing ✅ metrics track revenue impact, not just MQLa 🚀 example: Snowflake → multi-channel demand engine that created urgency around data cloud migration. Gong → blended inbound, outbound, and category creation to dominate sales tech. 2️⃣ seamless pipeline conversion → how do we ensure pipeline turns into revenue? ✅ sales process is mapped to buyer journey (not internal quotas) ✅ deal velocity, conversion rates, and forecast accuracy are measured weekly ✅ marketing doesn’t just generate leads—it owns pipeline acceleration 🚀 example: HubSpot → inbound marketing aligned with a structured sales handoff for faster close rates. Stripe → self-serve and sales-led motions work together to maximize growth. 3️⃣ revenue retention & expansion → how do we grow customers beyond their first purchase? ✅ net revenue retention (nrr) > new arr focus ✅ cs and sales align on customer expansion playbooks ✅ partnerships, integrations, and upsells create ongoing growth 🚀 example: Datadog → started with monitoring, expanded into full observability & security. Shopify → moved from a website builder into a full commerce ecosystem with payments, banking, and financing. final thoughts 📌 if your gtm motion isn’t predictable, scalable, and repeatable, you don’t have a system—you have tactics. 📌 if your teams operate in silos, you don’t have a system—you have friction. 📌 if you can’t measure efficiency, you don’t have a system—you have guesswork. GTM isn’t about launching a strategy. it’s about building a system. so i’ll ask you: is your gtm running on tactics, or are you building a system? let’s discuss 👇 love, sangram p.s. follow Sangram Vajre to learn how to fix your broken GTM with GTM O.S. #gotomarket #gtm #growth #b2b #sales #marketing

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?