Information Architecture Basics

There are three patterns for Multi-agent architectures that are more likely to deliver results in real-world applications today Understanding how multi-agent systems work together is important. Most people focus on what individual AIs can do, not how they collaborate. The architecture you select decides if your system provides value or turns into a costly failure. 🔹 1. Hierarchical systems are similar to team product launches. A central leader assigns specific tasks to different agents, such as market research, content creation, scheduling, and design. Then, it brings their results together into a unified product. This method works well when you have complex tasks with clear boundaries and dependencies. 🔹 2. Human-in-the-Loop systems pause for human insight at critical points. When an AI agent spots a client issue, it writes a response but needs human approval before sending it. This isn't about the limits of AI; it’s about knowing when human judgment is essential in important situations. 🔹 3. Sequential architectures function like assembly lines. Each agent performs a specific task before passing it along. Support tickets go through distinct stages, including initial draft creation, history review, solution development, and CRM logging. Each agent uses its skill without overlap or confusion. The key decision is to match your architecture to the complexity of the workflow and your comfort with risk. Use sequential for standard processes, hierarchical for complex coordination, and human-in-the-loop for critical decisions. Your choice of architecture affects whether multi-agent systems boost productivity or create coordination problems. #aiagents

TIPS FROM THE AGENCY (https://lnkd.in/gsyMAU5u) A few simple fixes to your site’s structure could lead to a 15x increase in organic traffic. That’s exactly what my agency, The Search Initiative, helped one of our clients do. They were running a web consulting service…but a messy site structure, broken links, and missing pages were killing their rankings. Here’s exactly how we 15x’d their traffic in just 1 year (you can implement this right away): 1. Website Structure Overhaul ✅ We audited the entire site to uncover missing service pages, redundant URLs, and dead-end navigation. This ensured that users found what they were looking for, and in turn, enhanced UX. ✅ Breadcrumb navigation was fixed, making the site easier for users (and search engines) to crawl. 2. Content Fixes That Boosted Engagement ✅ We restored and expanded individual service pages, replacing one overcrowded page with detailed, dedicated pages. This allowed us to increase visibility for service-related keywords. ✅ Each page was formatted with structured layouts, CTAs, and visuals to boost engagement and conversions. 3. Internal Linking Optimization ✅ We identified and eliminated orphan pages (pages undiscoverable by Google because they don't have any links pointing to them), ensuring every key page was properly linked. ✅ Duplicate content issues were resolved by merging similar pages. 4. Sitemap and Technical Fixes ✅ We removed redundant URLs and submitted a clean sitemap to Google Search Console. ✅ Ensured proper indexing of pages to reduce crawl errors. With proper site structure, stronger internal linking and optimized content… Traffic increased by 1,431% and engaged sessions rose 1,246%. If you’re struggling for online visibility and organic traffic, head over to: https://lnkd.in/gsyMAU5u We’ll provide a free audit of your website and custom strategies to scale your traffic right away.

SEO & AI Tip: Topical authority becomes much easier to manage when you can see your entire site as a semantic map. Most sites do not drift off topic because of one bad page. They drift slowly. One old blog post. One campaign page. One outdated resource. One service-adjacent topic that made sense three years ago. One content calendar that kept publishing, but never cleaned up. Over time, your site may start sending Google a different message than the one your business actually wants to send. The screenshot below is a semantic map of a domain with 318 pages. Each dot is a page. The center is the site’s core topic. The farther a page moves from the center, the more it starts drifting away from the main topical focus. In this example, the site is actually pretty healthy: Core: 48 pages Focus: 218 pages Expansion: 46 pages Peripheral: 6 pages That means the majority of the site is tightly aligned around the core topic, with only a small number of pages sitting far outside the main semantic footprint. What this reveals: - Topical authority is about more than just publishing more content. - It requires managing the content over time and ensuring all of it aligns with business goals. A site can have hundreds or thousands of pages and still be semantically focused. But it can also have years of content that slowly pulls the site into topics the business no longer cares about, no longer sells, or no longer wants Google associating with the brand. Why this matters: - Google does not evaluate pages in isolation. - Your site structure, internal links, content patterns, entities, and repeated topical signals all help shape what the site appears to be about. - if your old content is drifting too far from your current business focus, it may be creating noise around your expertise. This is where semantic mapping becomes useful. You can see: 1 - Which pages reinforce your core topic 2 - Which pages support adjacent expansion 3 - Which pages are drifting too far from the business 4 - Which sections may need pruning, consolidation, redirects, or internal linking 5 - Whether your content strategy is building authority or spreading attention too thin How to create something similar: 1 - Crawl your site with a tool like Screaming Frog 2 - Extract the main content from every indexable page 3 - Create a vector embedding for each URL 4 - Reduce the embeddings into 2D space 5 - Plot each page as a data point 6 - Calculate distance from the site’s semantic center 7 - Bucket pages into core, focus, expansion, and peripheral zones This allows for the foundation of a semantic management system you can track over time as more content is created. You can see semantic drift, you can manage it.

Technical SEO: don't just submit the sitemap index file in Google Search Console, you'll need to submit all important sitemaps individually also. When I get access to a clients GSC, often for very large sites, I unfortunately don't always have access to the indexing data that I need from the get-go. A core reason for this is when only the sitemap index file has been submitted, and not the individual sitemaps for core parts of a site e.g. products, categories, articles, guides etc. This approach effectively applies to all types of sites. Whether it be news publishers (where articles are often grouped within sitemaps by date), eCommerce sites, marketplaces, or sites of any kind. When you think about large sites that are maxing out on the amount of pages that they're squishing into a sitemap (50K is the limit), you're losing a great deal of insight along the way, especially if you're only able to view page indexing for millions of URLs at a time. I actually think that Google should really do this automatically when sitemaps are detected within the index, due to the usefulness of the reporting, but I do also understand there it does require input from the site owner in the first instance. If you haven't added your sitemaps to GSC in this way yet, get it over and done with and make the change now. If you end up working with someone in a technical SEO engagement, they will be thankful that you did (if they understand the importance of this reporting view).

🚨 System Design Mastery: 10 Concepts That Saved My Career (And Might Just Save Yours) 💥 When my startup failed after 4 years, I blamed marketing. The truth? 70% of the damage was system design. Since joining Google, I've seen what real scalable systems look like. Here are the lessons I wish I learned before I paid for them in downtime and lost users: 🔹 1. Scalability & Availability My biggest mistake? Building for 1M users when we had 5K. → At Google, we build for billions—but only when it matters. → Learn CAP Theorem. Understand your version of “99.9% uptime.” 🔹 2. Architecture Patterns Microservices almost bankrupted us. → Monolith = easy to debug. → Microservices = scalable, but complex. → Event-driven = great for async flows. Choose based on your team size, not Twitter threads. 🔹 3. Scaling Techniques We sharded too early and bled engineering hours. → Start simple: vertical scaling. → Scale smart: only when metrics say so. → Add complexity after stability. 🔹 4. Databases I lost 3 weeks to a production bug because I picked MongoDB for “flexibility.” → SQL vs. NoSQL isn’t the fight. → It’s about knowing your access patterns and consistency trade-offs. 🔹 5. Caching Redis cut our latency by 65%—but our invalidation logic nearly took us down. → Simple caching beats clever caching. → Track hit ratio. Monitor eviction. → TTL + LRU > overengineered mess. 🔹 6. Messaging Systems We relied on direct API calls. One failure = total collapse. → Kafka isn’t just a tool—it’s an insurance policy. → Decouple like your uptime depends on it. Because it does. 🔹 7. API Design We redesigned our API three times. → Good APIs evolve quietly. → Great ones don’t need to. → REST vs. GraphQL matters less than predictability and versioning. 🔹 8. Monitoring & Logging “The system’s down” used to be our status alert. → Now, Prometheus pings me before users even notice. → Invest in observability. Your team’s sanity depends on it. 🔹 9. Security Security isn’t a checklist—it’s a mindset. → Assume every input is malicious. → Plan for compromise, not just prevention. → At Google, we build like we’ve already been breached. 🔹 10. Trade-offs The hardest engineering lesson: No perfect systems exist. → You’re not designing the best system. → You’re designing the right one—for now. 📌 Save this before your next system design interview. ♻️ Repost to help another engineer avoid your old mistakes. 👇 What’s the system design trap that almost broke your build? 👤 Follow Abhishek Kumar for scar-tissue-backed lessons on tech, scale, and leadership.

You can memorize patterns and still build systems that fall apart. Because real system design comes in levels. ⬆️level 0 Fundamentals: • Clients send requests • Servers handle logic • Databases store data • Auth & Input validation You learn HTTP methods, status codes, and what a REST API is. You pick between SQL and NoSQL without really knowing why. You're not a backend dev until you've panic-fixed a 500 error in production caused by a missing null check. ⬆️level 1 Master the building blocks: • Load balancers for traffic distribution • Caches (Redis, Memcached) to reduce DB pressure • Background workers for async jobs • Queues (RabbitMQ, SQS, Kafka) for decoupling • Relational vs Document DBs; use cases, not just syntax differences You realize reads and writes scale differently. You learn that consistency, availability, and partition tolerance don't always play nice. You stop asking "SQL or NoSQL?" and start asking “What are the access patterns?” ⬆️level 2 Architect for complexity: • Separate read and write paths • Use circuit breakers, retries, and timeouts • Add rate limiting and backpressure to avoid overload • Design idempotent endpoints You start drawing sequence diagrams before writing code. You stop thinking in services and start thinking in boundaries. ⬆️level 3 Design for reliability and observability: • Add structured logging, metrics, and traces • Implement health checks, dashboards, and alerts • Use SLOs to define what “good enough” means • Write chaos tests to simulate failure • Add correlation IDs to trace issues across services At this level, you care more about mean time to recovery than mean time between failures. You understand that invisible systems are the most dangerous ones. ⬆️level 4 Design for scale and evolution: • Break monoliths into services only when needed • Use event-driven patterns to reduce coupling • Support versioning in APIs and messages • Separate compute from storage • Think in terms of contracts, not code • Handle partial failures in distributed systems You design for change, not perfection. You know your trade-offs. You know when to keep it simple and when to go all in. What’s one system design lesson you learned the hard way?

💡 How to design information architecture (5-step checklist) Information architecture is the practice of organizing, structuring, and labeling content in an effective way. Effective IA is crucial if you want to design an intuitive website or app. Here is a checklist to guide you through the IA design: 1️⃣ Understand user needs & the context of interaction Understand who your target user, their mental model, and how they interact with the information you have.  ✔ Create user personas that represent your target audience ✔ Conduct user research to gather insights into user needs, behaviors, and goal to understand the mental model (https://lnkd.in/dhCPA5T9)  ✔ Map out user journeys to understand the paths users take to achieve their goals 2️⃣ Content inventory & audit Analyze the content you have at a moment  ✔ Perform a content inventory to list all the items (pages, files, videos, etc) on your site or app ✔ Conduct a content audit to evaluate the quality and relevance of your content ✔ Identify gaps in your content that need to be filled to meet user needs 3️⃣ Content categorization & structuring Categorize content into groups that make sense to your target audience  ✔ Define the main categories of your content based on user needs and content audit findings ✔ Decide on the navigation schemes (e.g., hierarchical, sequential, matrix) based on the user's tasks ✔ Develop a labeling system that works well for the user (aligned with user language) ✔ Conduct card sorting sessions with your target audience to evaluate the labeling system (https://lnkd.in/d96mcwFJ) 4️⃣ Design navigation that reflects the structure of your content Build a navigation system that helps the user navigate through the content  ✔ Structure navigation hierarchically (from general to specific) ✔ Design a global navigation system that allows users easy access the main sections ✔ Design local navigation for navigating within sections. ✔ For complex navigation structures, use breadcrumbs to help users understand their current location and navigate back through the hierarchy ✔ Ensure the navigation system is both consistent across the site/app and scalable so it can accommodate the needs of your product 5️⃣ Usability testing ✔ Conduct usability tests to see how easily users can navigate your site or app and find information (measure both findability and discoverability) ✔ Use realistic test scenarios that reflect typical tasks users would perform on your site/app ✔ Collect quantitative data (e.g., task completion rates, time on task) and qualitative feedback (e.g., user comments, suggestions) ✔ Analyze the data to identify patterns, common usability issues, and areas for improvement 📖 Guides ✔ Practical guide to information architecture (by Donna Spencer) https://lnkd.in/dm9CE-TU ✔ Information architecture guide for product designers (YouTube) https://lnkd.in/dzJKXe8s 🖼️ Designing IA by Chen Ye #UX #uxdesign #design #UI #IA #uidesign

Modern data systems are not just built with tools - they’re built with design patterns that ensure reliability, scalability, and clarity as pipelines grow more complex. Here’s the breakdown of the core Data Engineering Design Patterns every engineer should understand. Each pattern solves a specific challenge across ingestion, storage, transformation, orchestration, quality, and scalability. Here’s a concise overview of the patterns: 1. Ingestion Design Patterns Data enters systems in different ways depending on freshness and volume needs. Batch ingestion handles scheduled loads, streaming ingestion captures real-time events, and CDC captures only row-level changes - ensuring efficient, timely, and fault-tolerant data collection. 2. Storage Design Patterns Choosing the right storage model shapes everything downstream. Data lakes keep raw, flexible data; data warehouses offer structured, analytics-ready storage; and lakehouses bridge both worlds by combining schema flexibility with high-performance querying. 3. Transformation Design Patterns ETL and ELT define when and where transformations happen. ETL transforms data before loading for strict governance, while ELT loads raw data first for faster, scalable cloud-based processing. Incremental processing focuses only on changed data to improve efficiency. 4. Orchestration & Workflow Patterns Pipelines require coordination. DAG-based workflows define execution order clearly, while event-driven patterns trigger pipelines based on system activity rather than schedules - improving responsiveness and decoupling systems. 5. Reliability & Fault-Tolerance Patterns Failure is inevitable, so pipelines must be resilient. Idempotent pipelines ensure repeated runs produce the same results, retry and dead-letter patterns detect or recover from failures, and backfill patterns safely reprocess historical data when needed. 6. Data Quality & Governance Patterns Trustworthy pipelines depend on clean, governed data. Validation enforces correctness, schema evolution handles safe structural changes, and lineage tracks how data flows - enabling debugging, compliance, and confident analytics. 7. Serving & Consumption Patterns How data is exposed matters as much as how it's processed. Semantic layers provide consistent business definitions, while API-based serving enables secure, controlled access for apps and downstream systems. 8. Performance & Scalability Patterns Systems grow, and patterns keep them fast. Partitioning improves query performance by slicing data, while caching accelerates repeated lookups and reduces compute cost. 9. Cost Optimization Patterns Efficient systems balance performance with spend. Tiered storage moves cold data to cheaper layers, and on-demand compute scales resources only when needed - reducing waste and controlling cost. These patterns form the foundation of modern data platforms - helping engineers design pipelines that are scalable, reliable, and easy to evolve.

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?

Trying to decide how to structure your AI agents for complex tasks? Not all agent setups are created equal. Whether you're building research assistants, automation workflows, or reasoning agents—your architecture matters. Here's a breakdown of 6 proven multi-agent structures and when to use them. 1. Simple Agent A single agent powered by an LLM calls tools to complete tasks. Easy to implement, but doesn’t scale well for complex jobs. 2. Network Multiple agents operate in a loop, sharing information directly. Great for peer collaboration, distributed reasoning, and exploration. 3. Supervisor One central agent delegates subtasks to others. Best for coordination, task management, and quality control. 4. Supervisor (As Tools) A supervisor agent is invoked like a tool by another agent. Enables modularity and expert-like behaviors embedded in other flows. 5. Hierarchical Agents are arranged in parent-child layers across levels. Ideal for structured workflows, decision trees, or step-by-step task pipelines. 6. Custom Mix and match multiple architectures to fit your domain. Perfect when flexibility and domain-specific logic are key. ✅ Use this cheat sheet to pick the right multi-agent architecture based on your use case, task complexity, and need for modularity or scalability.