API Integration Challenges

There’s a truth in our industry that nobody wants to say out loud: We don’t have a product problem. We have an integration problem. I sat through a presentation recently by Steve Greenfield, where he put up a slide listing every vendor touching the dealership ecosystem. 500 companies. I looked at that board and thought, “I’m paying every one of them.” That’s the problem. Every new “solution” creates a new silo. Every silo creates friction. And every bit of friction lands directly on the customer’s experience and on the backs of our teams. We’ve let complexity grow faster than value. And here’s the part that should make every dealer stop in their tracks: These vendors charge us tolls to use our own data. For our information. For our customer relationships. The PII, the personal identifiable information is the key to everything: Better service. Faster transactions. Cleaner processes. Stronger retention. And we’ve given that key away too easily. Not anymore. At Paragon, we’re stripping the system back to what actually matters: • Fewer partners • Zero vendors • Full integration • No duplication • No friction • No tolls on our data • A single ecosystem built for speed, clarity, and customer experience I watched a team member switch through ten different windows just to complete a simple transaction. Ten windows. One customer. One task. It’s absurd. It’s not innovation. It’s not efficiency. It’s not progress. It’s what happens when you build a business around tools instead of building tools around a business. The future isn’t more software. It’s smarter integration. It’s aligning human intelligence with machine intelligence so the customer gets the fastest, cleanest, most seamless experience possible. Because at the end of the day, that’s our job: Protect the customer. Protect the team. And remove anything that gets in the way of both. This is operational survival. And the dealerships that understand that first will be the ones standing tallest tomorrow.

A candidate interviewing for a Senior Engineer @ Meta was asked to design a rate limiter. Another candidate at Google's L5 loop got hit with the same question. I've been asked this three times across different companies. Rate-limiting questions look simple until you add one layer of complexity: – Add distributed rate limiting? Now you're dealing with race conditions and clock skew. – Add multiple rate limit tiers? Welcome to priority queues and quota management. – Add per-user, per-IP, and per-API-key limits? Your Redis bill just exploded. Here's my personal checklist of 15 things you must get right when building rate limiters: 1. Always do rate limiting on the server, not the client → Client-side limits are useless. They’re easily bypassed, so always enforce limits on your backend. 2. Choose the right placement → For most web APIs, place the rate limiter at the API gateway or load balancer (the “edge”) for global protection and minimal added latency. 3. Identify users correctly → Use a combination of user ID, API key, and IP address. Apply stricter limits for anonymous/IP-only clients, higher for authenticated or premium users. 4. Support multiple rule types → Allow per-user, per-IP, and per-endpoint limits. Make rules configurable, not hardcoded. 5. Pick an algorithm that fits your needs → Know the pros/cons: –  Fixed Window: Easy, but suffers from burst issues. –  Sliding Log: Accurate, but memory-heavy. –  Sliding Window Counter: Good balance, small memory footprint. – Token Bucket: Handles bursts and steady rates, an industry standard for distributed systems. 6. Store rate limit state in a fast, shared store → Use an in-memory cache like Redis or Memcached. Every gateway instance must read and write to this store, so limits are enforced globally. 7. Make every check atomic → Use atomic operations (e.g., Redis Lua scripts or MULTI/EXEC) to avoid race conditions and double-accepting requests. 8. Shard your cache for scale → Don’t rely on a single Redis instance. Use Redis Cluster or consistent hashing to scale horizontally and handle millions of users/requests. 9. Build in replication and failover → Each cache node should have replicas. If a primary fails, replicas take over. This keeps the system available and fault-tolerant. 10. Decide your “failure mode” → Fail-open (let all requests through if the cache is down) = risk of backend overload. Fail-closed (block all requests) = user-facing downtime. For critical APIs, prefer fail-closed to protect backend. 11. Return proper status codes and headers → Use HTTP 429 for “Too Many Requests.” Include headers like: – X-RateLimit-Limit,  – X-RateLimit-Remaining,  – X-RateLimit-Reset, Retry-After This helps clients know when to back off. 12. Use connection pooling for cache access → Avoid reconnecting to Redis on every check.  Pool connections to minimize latency. Continued in Comments...

New Update: Amazon DSP campaign and creative APIs are now generally available. This is a build on many of the announcements from #unBoxed2024 What is it? This new feature allows users to create, read, and update their Amazon DSP campaigns, ad groups, targets, and creatives through a programmatic interface. How does it work? These APIs enable technology providers and advertisers to develop custom experiences within their own applications and seamlessly run Amazon DSP campaigns within existing workflows. The new APIs can be used in conjunction with existing audience and deal resources, providing a comprehensive toolkit for end-to-end campaign management. Users can now store Amazon DSP campaign data locally, simplify campaign and creative creation, and automate optimizations to maximize campaign performance. Why should I care? This update is a game-changer for Amazon DSP users. Here's why it matters: 1. Efficiency boost: Streamline your campaign and creative creation process, significantly reducing activation time for new campaigns. 2. Better data control: Store and manage Amazon DSP campaign data locally, giving you more control over your data and analytics. 3. Custom optimization: Automate optimizations across campaign, ad group, and targeting settings, allowing for data-driven decisions on bids and budgets. 4. Seamless integration: Easily integrate Amazon DSP into your existing tech stack, enabling you to track campaigns in your own tools and sync campaign metadata with your data storage solutions. 5. Performance improvement: Experiment with new audiences and quickly remove underperforming ones to maximize campaign performance. 6. Real-time adjustments: Automatically adjust bids and budgets in real-time, ensuring your campaigns are always performing at their best. Bottom line: Whether you're a large agency or tech partner looking to integrate Amazon DSP more deeply into your operations or an individual advertiser seeking to automate and optimize your campaigns, these new APIs offer exciting possibilities to enhance your advertising efforts on Amazon's platform. Want to check it out? You can learn more about these new features at the Amazon Ads website (https://lnkd.in/gESdMWhy). For those ready to dive in, check out the developer guide (https://lnkd.in/gQAPRdcs) and reference documentation (https://lnkd.in/gBV-BbVb) to start leveraging these powerful new APIs in your advertising strategy.

Vulnerabilities in MCP (Model Context Protocol) I was hired to audit integrations of an LLM with MCP, for use with data management tools, log collections and automated routines. Here are some problems I found and would like to share so that those of you who want to implement MCP in your products can start thinking about security at the beginning of the development cycle. However, it is worth mentioning that there are still not many efficient solutions, despite some selling LLM Firewalls. I would like to test and validate the effectiveness of this. Anyway, let's get to the points: 1) The lack of HTTPS in API Integrations was a problem I noticed a lot. The LLM and the integrated MCP APIs that were integrated with the tools or executed commands and received the response to the commands allowed me to view the requests and responses. I used Wireshark to validate. 2) Inadequate Permission Management, allowing me to access data from other clients without any tenant isolation, all via Prompt Injection and Burp Suite to analyze requests and perform basic manipulations. 3) Abuse of Automations and Unrestricted Resource Consumption, allowing me to trigger multiple parallel routines, all via a single prompt, or sending different prompts causing the server to trigger routines all at once, without proper thread queue management. I used Burp Suite with Intruder and created a list of prompts and executed at least 50 different prompts with the same context. In addition, there was no control over the request limit in the APIs. 4) SQL Injection via Prompt, basically making requests using human language, for example: “what columns does the users table have?” resulted in queries being executed directly without control and spitting out information, i.e., it seems that the integration opened the database schema (weird). Obviously, the problem is that it built the query in the backend and processed it as an SQL query. I used Burp Suite in this case to analyze the response, etc. 5) Hardcoded Secrets in the MCP Code. API tokens, database credentials, and endpoints were found directly in the MCP integration scripts. Although it is obvious, just because they are in the backend does not mean they must be hardcoded. Unfortunately, I was unable to extract secrets via prompt injection or obtain an RCE. 6) Broad Context allowing Full Control of the application. Although I did not obtain the application secrets, providing broad context to the LLM gave it full control over the integrated systems, executing tasks that should be exclusive to the admin, since the configured keys had excessive permissions that allowed the execution of numerous functions. In short, these are flaws that a trained developer with knowledge of application security could resolve, but many who start integrating solutions with AI do not worry about Shift-Left. #mcp #AI #redteam #cybersecurity #AISecurity #mcpsecurity #pentest #llmpentest

The biggest businesses can get major programmes horribly wrong. Here are 4 famous examples, the fundamental reasons for failure and how that might have been avoided. Hershey: Sought to replace its legacy IT systems with a more powerful ERP system. However, due to a rushed timeline and inadequate testing, the implementation encountered severe issues. Orders worth over $100 million were not fulfilled. Quarterly revenues fell by 19% and the share price by 8% Key Failures: ❌ Rushed implementation without sufficient testing ❌ Lack of clear goals for the transition ❌ Inadequate attention and resource allocation Hewlett Packard: Wanted to consolidate its IT systems into one ERP. They planned to migrate to SAP, expecting any issues to be resolved within 3 weeks. However, due to the lack of configuration between the new ERP and the old systems, 20% of customer orders were not fulfilled. Insufficient investment in change management and the absence of manual workarounds added to the problems. This entire project cost HP an estimated $160 million in lost revenue and delayed orders. Key Failures: ❌ Failure to address potential migration complications. ❌ Lack of interim solutions and supply chain management strategies. ❌ Inadequate change management planning. Miller Coors: Spent almost $100 million on an ERP implementation to streamline procurement, accounting, and supply chain operations. There were significant delays, leading to the termination of the implementation partner and subsequent legal action. Mistakes included insufficient research on ERP options, choosing an inexperienced implementation partner, and the absence of capable in-house advisers overseeing the project. Key Failures: ❌ Inadequate research and evaluation of ERP options. ❌ Selection of an inexperienced implementation partner. ❌ Lack of in-house expertise and oversight. Revlon: Another ERP implementation disaster. Inadequate planning and testing disrupted production and caused delays in fulfilling customer orders across 22 countries. The consequences included a loss of over $64 million in unshipped orders, a 6.9% drop in share price, and investor lawsuits for financial damages. Key Failures: ❌ Insufficient planning and testing of the ERP system. ❌ Lack of robust backup solutions. ❌ Absence of a comprehensive change management strategy. Lessons to be learned: ✅ Thoroughly test and evaluate new software before deployment. ✅ Establish robust backup solutions to address unforeseen challenges. ✅ Design and implement a comprehensive change management strategy during the transition to new tools and solutions. ✅ Ensure sufficient in-house expertise is available; consider capacity of those people as well as their expertise ✅ Plan as much as is practical and sensible ✅ Don’t try to do too much too quickly with too few people ✅ Don’t expect ERP implementation to be straightforward; it rarely is

McKinsey's ERP warning for CFOs: 1. 70% of ERP transformations fail     Most ERP projects run over budget and underdeliver. Why? Because companies underestimate complexity. Finance expects a big bang switch. Instead, they get endless data cleanups, mismatched chart of accounts, and broken workflows. In finance, a 90% rollout isn’t a win. If one close process breaks, the whole system stalls.     2. It's your design, not your tech     CFOs blame vendors. But the real issue is design. Too many teams lift-and-shift old processes into new systems. That hardcodes inefficiency. The 30% who succeed don’t copy the past. They redesign approvals, reconciliations, and controls before go-live. ERP isn’t a tool migration. It’s an operating model redesign.     3. Finance feels the pain first     In sales, if CRM misses a field, people workaround. In finance, if ERP misses a journal entry, you misstate results. Month-end closes, audits, and compliance magnify every flaw. That’s why ERP failures show up in finance before anywhere else. Unless you engineer accuracy and reliability from day one, the CFO’s credibility is at risk.     4. The gap turns critical     McKinsey calls it out: 70% stuck, 30% pulling ahead. The stuck companies run digital systems that replicate legacy pain. The winners embed automation, shared data models, and continuous improvement. Over time, that gap compounds into faster closes, lower costs, and better decision-making.     TAKEAWAY ERP failures don’t just cost money at go-live. They lock in inefficiencies for years. Every close takes longer. Every audit is harder. Every board deck gets delayed. The reverse is also true. When ERP is designed right, benefits compound: - Faster closes free capacity - Automation creates leverage - Cleaner data sharpens insight The real gap isn’t visible at launch. It shows up quarter after quarter, year after year.

From day one in #b2becommerce, this has been one of the biggest traps I’ve seen. Distributors: please don’t get pulled down the primrose path just because a platform says it has an “out-of-the-box” integration with your ERP. The truth no one talks about? There's really no such thing as an 'out-of-the-box' integration. Everyone uses ERP and eCom differently. So, at the least, there will be configuration. But there are several platforms still running this gig - old tech, proprietary, no ecosystem. They tempt you with a seemingly low-risk way to launch quickly. The bigger issue: What will you be left with in the end? Let’s be honest: integration sucks. But it happens successfully every day. And many ERP platforms have connectors (or other tools) to simplify this process. So chances are, you’re not starting from scratch regardless of which direction you go. But here’s the catch: That “easy” integration comes with old technology, clunky experiences, and heavy customization. And by the time you realize how much you’ve spent just to get it halfway usable, the golden handcuffs are on. You could easily be stuck with a platform your customers hate with no clear path forward. Don’t do that to your eCommerce business - or, more importantly, your customers. They deserve a great customer experience- modern, API-first, composable platforms that let you build the experience they actually want. At the very least, do an honest, thorough evaluation against other B2B platforms, specific to your requirements. Because in the end, it’s not about what it costs. It’s about what you can make. #B2BEcommerce #B2BPlatforms #ERPIntegration #DigitalTransformation #CustomerExperience #MidMarketDistributors #ComposableCommerce #PlatformSelection

🛑 "429 Too Many Requests" isn't just an error code; it's a survival strategy for your distributed systems. Stop treating Rate Limiting as a simple counter. To prevent crashes, you need the right algorithm. This visual explains the patterns you need to know. 𝐇𝐨𝐰 𝐰𝐞 𝐜𝐨𝐮𝐧𝐭: 1️⃣ Token Bucket: User gets a "bucket" of tokens that refills at a constant rate. Great for bursty traffic. If a user has been idle, they accumulate tokens and can make a sudden burst of requests without being throttled immediately. Use Case: Social media feeds or messaging apps. 2️⃣ Leaky Bucket: Requests enter a queue and are processed at a constant, fixed rate. Acts as a traffic shaper. It smooths out spikes, protecting your database from write-heavy shockwaves. Use Case: Throttling network packets or writing to legacy systems. 3️⃣ Fixed Window: A simple counter resets at specific time boundaries (e.g., the top of the minute). Easiest to implement but suffers from the "boundary double-hit" issue (e.g., 100 requests at 12:00:59 and 100 more at 12:01:01). Use Case: Basic internal tools where precision isn't critical. 4️⃣ Sliding Window Log: Tracks the timestamp of every request. Solves the boundary issue completely. It’s highly accurate but expensive on memory (O(N) space complexity) because you store logs, not just a count. Use Case: High-precision, low-volume APIs. 5️⃣ Sliding Window Counter: The hybrid approach. Approximates the rate by weighing the count of the previous window and the current window. Low memory footprint, high accuracy. Use Case: Large-scale systems handling millions of RPS. 𝐖𝐡𝐞𝐫𝐞 𝐰𝐞 𝐞𝐧𝐟𝐨𝐫𝐜𝐞 6️⃣ Distributed Rate Limiting: Essential for microservices. You cannot rely on local memory; you need a centralized store (like Redis with Lua scripts) to maintain a global count across the cluster. 7️⃣ Fixed Window with Quota: Often distinct from technical throttling. This is business logic—hard caps over long periods (months/years). Use Case: Tiered billing plans (e.g., "Free Tier: 10k calls/month"). 8️⃣ Adaptive Rate Limiting: The "smart" limiter. It doesn't use static numbers but monitors system health (CPU, memory, latency). If the system struggles, it tightens the limits automatically. Use Case: Auto-scaling systems and disaster recovery. 𝐖𝐡𝐨 𝐰𝐞 𝐥𝐢𝐦𝐢𝐭 9️⃣ IP-Based Rate Limiting: The first line of defense. Limits based on the source IP to prevent botnets or DDoS attacks. Use Case: Public-facing unauthenticated APIs. 🔟 User/Tenant-Based Rate Limiting: Limits based on API Key or User ID. Ensures one heavy user doesn't degrade performance for others ("Noisy Neighbor" problem). Use Case: SaaS platforms and multi-tenant architectures. 💡 For most production systems, Sliding Window Counter combined with Distributed Limiting is the gold standard. It offers the best balance of memory efficiency and user fairness. #SystemDesign #SoftwareArchitecture #API #Microservices #DevOps #BackendEngineering #RateLimiting #CloudComputing

𝗠𝗖𝗣-𝗘𝗡𝗔𝗕𝗟𝗘𝗗 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦 𝗙𝗔𝗜𝗟 40-60% 𝗢𝗙 𝗧𝗛𝗘 𝗧𝗜𝗠𝗘 𝗢𝗡 𝗥𝗘𝗔𝗟-𝗪𝗢𝗥𝗟𝗗 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪𝗦: 𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗬 My daily work on LLM's workflow architectures (MCP-driven agent workflows) pushes me to the frontier of how 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 (𝗠𝗖𝗣𝘀) can be reliably exploited at scale. The 𝗟𝗶𝘃𝗲𝗠𝗖𝗣-101 study (arXiv:2508.15760) offers valuable insights into this challenge. 𝗕𝗘𝗡𝗖𝗛𝗠𝗔𝗥𝗞 - LiveMCP-101, a benchmark of 101 carefully curated real-world 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 queries 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗮𝘀𝗸𝘀 (average 5.4 steps, up to 15) stress-test MCP-enabled agents across web, file, math, and data analysis domains. - 18 𝗺𝗼𝗱𝗲𝗹𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱: OpenAI, Anthropic, Google, Qwen3, Llama. 𝗞𝗘𝗬 𝗙𝗜𝗡𝗗𝗜𝗡𝗚𝗦 - 𝗚𝗣𝗧-5 𝗹𝗲𝗮𝗱𝘀 with 58.42% Task Success Rate, dropping to 39.02% on "Hard" tasks - 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗹𝗮𝗴𝘀 𝗯𝗲𝗵𝗶𝗻𝗱: Qwen3-235B at 22.77%, Llama-3.3-70B below 2% - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗽𝗹𝗮𝘁𝗲𝗮𝘂: Closed models plateau after ~25 rounds; open models consume more tokens without proportional gains 𝗖𝗢𝗡𝗖𝗥𝗘𝗧𝗘 𝗧𝗔𝗦𝗞 𝗘𝗫𝗔𝗠𝗣𝗟𝗘𝗦 - 𝗘𝗮𝘀𝘆: Extract latest GitHub issues - 𝗠𝗲𝗱𝗶𝘂𝗺: Compute engagement rates on YouTube videos - 𝗛𝗮𝗿𝗱: Plan an NBA trip (team info, tickets, Airbnb constraints) with consolidated Markdown report 𝗙𝗔𝗜𝗟𝗨𝗥𝗘 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦 - 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗿𝗿𝗼𝗿𝘀: Skipped requirements, wrong tool choice, unproductive loops - 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀: Semantic (16.83% for GPT-5, up to 27.72% for other models) and syntactic (up to 48.51% for Llama-3.3-70B) - 𝗢𝘂𝘁𝗽𝘂𝘁 𝗲𝗿𝗿𝗼𝗿𝘀: Correct tool results misinterpreted 𝗧𝗔𝗞𝗘𝗔𝗪𝗔𝗬𝗦 𝗙𝗢𝗥 𝗠𝗖𝗣 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪 𝗗𝗘𝗦𝗜𝗚𝗡 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, is the main bottleneck. Reliability requires: • External planning • Tool selection, ranking and routing (RAG-MCP, ...) • Variable passing between MCP & memory (Variables Chaining) • Schema validation • Trajectory monitoring • Efficiency policies, Budget-aware execution 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: The path forward isn't adding more tools, but engineering robust orchestration layers that make MCP chains dependable. What's your experience with AI agent workflows at scale? Have you experienced similar failure patterns? Many of these orchestration issues are ones I’ve needed to tackle in practice — always happy to compare notes with others working on advanced solutions. Link to the paper: https://lnkd.in/g8bbNK6E #AI #MachineLearning #Workflows #MCP #AIAgents #Productivity #Innovation   

ERP won't streamline operations effortlessly. Without planning, it creates chaos instead. Most founders assume an ERP implementation will automatically fix revenue leakage and improve decision-making. The reality? Without proper planning, you get tangled data and frustrated teams. I've watched a founder plug in their ERP expecting magic. Instead: → Data became a mess → Employees grew frustrated → Decision-making got worse, not better The gap between expectation and execution comes down to three things: • No clear strategy before implementation • Lack of team buy-in from day one • Underestimating the complexity of system integration ERP systems are powerful tools for reducing revenue leakage and enabling better decisions - but only when you treat implementation as a strategic project, not a plug-and-play solution. The best founders don't assume technology will solve their problems. They build the strategy, align the team, and execute with precision. That's how you turn an ERP from a headache into a competitive advantage.