Software Design Patterns

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

I've been using Clean Architecture for 6+ years. Here’s why I think it’s amazing. 👇 The biggest pain in enterprise systems? A lack of structure. Every project reinvents the wheel. Every team builds layers differently. And knowledge doesn’t transfer between systems. But there’s a proven way to fix this. It’s called Clean Architecture. It’s not about how many projects you create. It’s not about fancy patterns. ✅ It’s about the direction of dependencies. Inner layers (domain, app) define abstractions. Outer layers (infra, presentation) implement those abstractions. Never the other way around. That’s it. That’s the rule. You can package this as: - Layers (domain, app, infra, web) - Vertical slices (grouped per feature) - Components (layers + vertical slices) They all work — if you follow the rule. What are the benefits? - Modular code - Clear separation of concerns - Easy-to-test business logic - Faster onboarding - Loosely coupled components Clean Architecture has helped me ship excellent products. And I’ll keep using it because it works. Want to simplify your development process? Grab my free Clean Architecture template here: https://lnkd.in/eDgfyWKB

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

ORMs are an anti-pattern once you start operating at scale. At scale, most move away from them as the abstraction that bridges the database and programming language never fully holds. At scale, by using ORM, you almost always end up missing native database optimizations. And if you are already writing raw queries to work around ORM limitations, you are actually better off using prepared statements directly. The ORM abstraction hides query complexity, yes, but it does make it easy to ship inefficient N+1 queries without realizing it. Debugging becomes painful at the worst possible time - during production incidents. ORMs tend to lag behind database features. Indexing strategies, query hints, and newer capabilities are either inconvenient to use (just look at prefetch in Django ORM) or simply unavailable. This makes you opt for the lowest common capability (as offered by ORM) instead of leveraging the database properly. At scale, explicit queries are always simpler, faster, and easier to optimize; the cognitive overhead of mapping objects to tables is often not worth it. To be fair, ORMs make sense for prototypes and for getting to market quickly. Use them when you are starting out or in the early phases, but have a plan to migrate away from them :) Easier said than done, though; been there!

🌟 New Paper: AI Agents vs. Agentic AI Interesting paper summarizing distinctions between AI Agents and Agentic AI. It also talks about the key ideas, solutions, and the future. Here are my notes: ⚪ What is the paper about? The paper provides a comprehensive taxonomy and comparison between AI Agents and Agentic AI, clarifying their conceptual, architectural, and operational differences. ⚪ What are AI Agents? AI Agents are single-entity systems enhanced with LLMs and external tool integration, capable of task-specific autonomy and sequential reasoning. They are reactive, modular, and typically used for narrow applications like email triage, scheduling, or customer service. ⚪ What is Agentic AI? Agentic AI represents an architectural shift. These systems involve multiple collaborating agents with dynamic task decomposition, persistent memory, and orchestration layers. They enable higher-level coordination and are suited for complex workflows like research automation, robotic swarms, and medical diagnostics. ⚪ Application Mapping AI Agents: Email filtering, report summarization, content recommendation, customer support. Agentic AI: Coordinated research assistants, ICU decision support, robotic orchard harvesters, adaptive game AIs. ⚪ Challenges AI Agents: Limited causal reasoning, hallucinations, lack of proactivity, brittle long-horizon planning. Agentic AI: Inter-agent error cascades, emergent instability, opaque communication, scalability, explainability, and security vulnerabilities. ⚪ Key Architectural and Algorithmic Solutions - Retrieval-Augmented Generation (RAG) - Tool-augmented reasoning (function calling) - Agentic Loop: Reasoning, Action, Observation - Memory Architectures (Episodic, Semantic, Vector) - Multi-agent orchestration with Role Specialization - Reflexive and Self-Critique Mechanisms - Programmatic Prompt Engineering Pipelines - Causal Modeling and Simulation-based Planning - Monitoring, Auditing, and Explainability Pipelines - Governance-aware design with role isolation and traceability There are all important areas that researchers and developers need to get familiar with to build reliable and robust agentic systems. ⚪ Future Roadmap For AI Agents: Proactive intelligence, continuous learning, trust & safety. For Agentic AI: Multi-agent scaling, simulation-based planning, ethical governance, and domain-specific systems. These are all areas that need huge innovations in algorithms, architectures, infrastructure, protocols, and enhancing the models themselves.

Not a joke, many Data Engineers don’t fully understand the Medallion architecture or their caveats. Here’s a simple, crisp breakdown of the Medallion Architecture and why each layer matters: 🔹 Bronze (Raw Ingestion) - All incoming data lands here-> logs, JSON, CSV, streaming events - Data stays in its original form (think Delta Lake tables) - Use schema-on-read to keep raw JSON/XML (no forced schema yet) - Partition by ingest date/hour for fast file pruning - Add audit columns (ingest_timestamp, source_file, batch_id) for full traceability Why care? Bronze is your “source of truth.” You can recover, reprocess, or track every record. 🔹 Silver (Cleansed & Curated) - Cleaned, standardized view of Bronze data - Enforce data types, drop nulls, fill defaults (schema-on-write) - Use joins and dedupe logic (window functions help remove duplicates) - Add data profiling and constraints (NOT NULL, CHECK) to stop bad data early Why care? Silver gives you reliable, consistent tables for analytics, reports, and ML models. 🔹 Gold (Business Aggregations) - Highly curated, aggregated tables or dimensional models - Pre-compute metrics (daily active users, revenue by region) - Use Slowly Changing Dimension (SCD) for customer data - Partition and Z-order in Delta for super-fast queries Why care? Gold delivers high-performance datasets for BI tools and ML feature stores. Key Benefits Across Layers 1. Modularity & Maintainability – Keep ingestion, cleaning, and aggregation logic separate 2. Data Quality – Catch issues step by step 3. Scalability – Stream and batch workloads scale on their own 4. Governance & Lineage – Track every change with audit columns and Delta logs What else you would like to add here ? 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝟭:𝟭 𝗳𝗼𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲 → https://lnkd.in/gH4DeYb4 𝗔𝗧𝗦 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗲𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲 → https://lnkd.in/g-iw7FaQ Gif -> Ilum ♻️ Found this useful? Repost it! ➕ Follow for more daily insights on building robust data solutions.

Agent memory has quickly become one of the most discussed topics in AI. As more teams start building real agent systems, one limitation keeps showing up: agents don’t remember much. Most agents today are essentially stateless. They can reason through tasks, call tools, and generate impressive responses, but once the session ends, the system forgets everything. And the conversation often gets simplified into two layers: 𝟏) 𝐒𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 = 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐰𝐢𝐧𝐝𝐨𝐰 This is where agents keep conversation history, reasoning steps, and recent tool outputs.  But context windows are temporary. 𝟐) 𝐓𝐡𝐞𝐧 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 This is where agents remember things across sessions: - user preferences - past interactions - knowledge collected over time - intermediate results from previous tasks And once you start thinking about long-term memory, the problem quickly shifts. This is really a data infrastructure problem. In many cases, long-term agent memory ends up living in the data layer. Which is why databases are starting to play a bigger role in modern AI systems. For example, 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 has been building more capabilities around this idea. By integrating vector search directly into the database, application data and embeddings can live in the same system instead of being split across multiple tools. That makes it easier to store agent memory, retrieve relevant context with vector search, and keep embeddings synchronized with the underlying data. For teams building AI systems, this kind of architecture reduces a lot of the complexity around memory. If you're exploring this space, MongoDB’s Learning Hub has some useful courses and hands-on labs worth checking out https://lnkd.in/gazxytWP As agents start running longer workflows, memory quickly becomes part of the system architecture. Designing how that memory is stored, retrieved, and updated may turn out to be one of the most important pieces of agent design. #aiagents #agenticai #machinelearning #data #database

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

Building a Payment Service from Scratch - What I Learned as a Founder When we started building the payment system at takeUforward, I thought it would be simple, just integrate Razorpay/Paypal and move on. But once we went deeper, it turned into a full product within a product. Payments are about reliability, data integrity, and how gracefully your system behaves under pressure. Here’s what we learned 👇 - Don’t build only for “plans”, design for plan entities in your schema so upgrades/bundles/emi’s/future standalone offerings are easier to integrate - Support multiple validity periods within the same flow. - Implement an Adapter Pattern so gateways like Razorpay, PayPal, or Stripe can be swapped without touching business logic. - Keep a transaction_id as the single source of truth and design every API around it (create, capture, refund, audit) - Log everything, and store gateway response for future auditing - Ensure idempotency for every payment and refund API - a single retry shouldn’t double charge a user - Run all external calls async using background jobs or message queues to avoid blocking the user flow - Integrate refund callbacks with GST reversal logic to maintain accurate tax credits - Implement signature validation and webhook verification to avoid spoofed callbacks - Support EMI and split payments, each transaction linked via a parent-child hierarchy - Build monitoring dashboards for transaction latency, failure rates, and P95 response times - Use feature flags to roll out new gateways or payment logic gradually - Integrate third party sales - Integrate other product sales on our platform, manage taxes for them. These things sound overkill at the start, until you hit scale and realize why systems like Stripe and Razorpay obsess over these details. 💡 Check out TUF+ - we’ve covered OOPs/LLD over there, along with everything needed for Interview Preparation. It’s your one-stop interview prep platform for DSA, LLD, Core CS and more. Use code STRIVER to get the maximum discount, valid for the next few hours only ⏰

"How to write a good spec for AI agents" My latest free write-up: https://lnkd.in/g--r4kEu ✍ Using an AI agent for coding or other use-cases? I keep hearing "just write a better spec" but the real problem isn't effort. You can write something that rivals an RFC and still watch the model fall apart once the context gets too large. What's worked for me is aiming for specs that are clear, scoped, and designed for how agents actually process information, not how humans wish they would. A few principles that consistently pay off: Start with a high-level vision, then let the agent draft the details. Treat the first pass like a product brief. Review, correct, and refine before any code is written. Structure the spec like a real PRD. Clear sections, explicit commands, testing expectations, style examples, and hard boundaries beat loose prose every time. Break work into modular tasks instead of one giant prompt. Small, focused context wins over dumping everything into a single message. Build in guardrails. Define "always do,” "ask first,” and "never do.” Encourage self-checks and verification against the spec before you review anything. Plan first, execute second. Use read-only or plan modes where possible, then iterate. Specs work best as living documents that evolve with the project. The takeaway: good agent specs aren't about writing more; they're about writing just enough structure and nuance to keep the model focused without overwhelming it. If you've been frustrated by agents drifting, hallucinating, or missing requirements, the fix is usually upstream. The spec is the first thing you and the AI build together. #ai #programming #softwareengineer