Scalability in Automated Support Systems

OpenAI doesn’t measure support the way you do. They’re not chasing CSAT or time-to-close. They rebuilt support — and what they came up with changes everything. Here’s the shift: A ticket opens. It gets solved. It closes. And most of the knowledge dies there. OpenAI saw that model couldn’t scale. Support wasn’t just a volume problem — it was an engineering and operational design problem. So they built something different: a system where every interaction improves the next. It starts with three building blocks: 🔲 Surfaces. Where support lives: chat, email, voice, and increasingly embedded directly in-product. 🔲 Knowledge. Not static docs, but living guidance that evolves with real conversations, policies, and context. 🔲 Evals & classifiers. Shared definitions of quality built by humans + software, continuously running to steer the system. These pieces form a loop. A pattern spotted in an enterprise chat updates the knowledge base. An eval created for one case trains the model for thousands more. And because the same primitives power every channel, improvements scale automatically. And here’s what really struck me: At OpenAI, reps aren’t just responding to tickets. They flag interactions that should become test cases. They propose new classifiers. They even prototype lightweight automations to close workflow gaps. Training shifts too — from just “policies” to spotting structural gaps and feeding improvements back. The result? Support isn’t measured by throughput, but by its capacity to evolve. And the loop doesn’t stop there. Each interaction compounds: Evals turn daily conversations into production tests. They codify what “great” means: not just solving, but solving politely, clearly, consistently. Patterns flow back into knowledge, automation, and product design. Every resolution strengthens the system. Every pattern spotted improves future answers. Every classifier scales across channels. And the org itself learns alongside the AI — reps shape classifiers, contribute datasets, and watch quality improve in real time through observability dashboards. What does all this point to? A blueprint for the future of support. Glen Worthington put it best: “Support has never really been about replying to just tickets. It’s about whether people get what they need, whether it actually serves them well.” That’s the profound shift: Support specialists are recognized not just for solving problems, but for refining knowledge, improving models, and extending the system itself. The future isn’t support as a destination. It’s support as an action — woven into every product surface. Here’s the uncomfortable question for every support leader 👇 If you look at your last 100 tickets… How many made tomorrow’s support better than today’s? Because in the future, the answer needs to be: all of them. Jay Patel Shimul Sachdeva

I’ve reviewed the approaches of 500+ candidates in system designs in interviews, and 80% of them always failed because they didn’t address at least 3 of these 6 bottleneck categories. Here’s how to avoid this mistake yourself using the SCALED framework. If your system design doesn’t address potential bottlenecks, it’s not complete. The SCALED framework helps you ensure your architecture is robust and ready for real-world demands.   1. Scalability   → Can your system handle growth in users or traffic seamlessly?   → Does it allow for adding resources without downtime?   → Are your APIs designed to work with distributed systems?  Example: Use consistent hashing for sharding so new servers can be added or removed without disrupting existing data.   2. Capacity (Throughput)   → Can your system manage sudden spikes in traffic?   → Are high-volume operations optimized to avoid overloading the system?   → Is there a mechanism to scale resources automatically when needed?  Example: Implement auto-scaling to handle upload/download spikes, triggered when CPU usage exceeds 60% for 5 minutes.  3. Availability   → Does your system stay functional even during failures?   → Are backups and redundancies in place for critical components?   → Can your services degrade gracefully instead of failing entirely?  Example: Use a replication factor of 3 in your database so it remains available even if one server goes down.  4. Load Distribution (Hotspots)   → Are you distributing traffic evenly across servers?   → Have you addressed potential bottlenecks in frequently accessed data?   → Are shard keys designed to avoid uneven load distribution?  Example: Shard data by photo_id instead of user_id to avoid overloading shards for high-traffic accounts like celebrities.  5. Execution Speed (Parallelization)   → Are bulky operations optimized with parallel processing?   → Are frequently accessed data items cached to reduce latency?   → Can large file operations (uploads/downloads) be split into smaller chunks?  Example: Use distributed caching like Redis to store frequently accessed data, serving 80% of requests directly from memory.  6. Data Centers (Geo-availability)   → Are your services available to users worldwide with low latency?   → Are data centers located close to users for faster access?   → Are static assets cached using CDNs for quicker delivery?  Example: Use CDNs to cache images and videos closer to users via edge servers in their region.  A solid system design doesn’t just solve problems, it predicts and handles bottlenecks.  Next time, don’t just design, SCALED it.

If I were the VP of Support at an enterprise company dealing with repetitive customer support tickets, here’s how I’d use AI to power KCS and improve ticket resolution while turning my support agents into “heroes”: First, some context: - Most support tickets are recurring, yet agents have to field every single one of them individually (this is unscalable).  - Agents are only rewarded based on the number of tickets resolved and have a hard time improving support quality (can be unrewarding) The best way to go about this problem? Enabling agents to externalize documentation on their own and improve support quality with every logged request, using AI to power Knowledge-Centered Support (KCS) Here’s how I’d implement this at an enterprise company: 1) Democratize knowledge creation Support agents know customer issues best, so it doesn’t make sense to wait for technical writers (who are already swamped) to create knowledge articles. With the help of AI, you can enable support agents to generate knowledge articles on their own, just by clicking a button. 2) Externalize new knowledge All new knowledge articles can be pushed to your external customer help center/knowledge hub right away. With that, customers can either resolve issues on their own or ask an AI Chatbot (that has immediate access to all knowledge articles). 3) Iterate & improve knowledge Now that recurring tickets are handled, support agents can dedicate their time to tickets that *actually* need human help. AI can then help them update existing articles as similar requests come in. This is WAY more efficient than relying on technical writers because your agents are already “on the ground.” 4) Gamify support process On the backend, AI can track & display: - Which customer issues were resolved  - Which knowledge articles were referenced - How many customers were assisted by each agent - How many tickets were resolved or deflected This makes it easier to boost support morale because agents see the REAL impact of what they’re doing for customers and the company – in short, they become “heroes.” (We do this ourselves at Ask-AI) TAKEAWAY An AI-powered KCS will help you improve your overall customer experience. You can resolve customer issues faster, your support agents are empowered – and the VP of support can report better TTR and CSAT metrics. Any thoughts on this?

A lot of people think the toughest part about deploying AI agents in enterprise environments is to figure out the best model to use - OpenAI vs Claude vs DeepSeek. Completely wrong. We have worked with top enterprises and multiple public companies to deploy AI support agents, and here’s what we’ve learned: the real question isn’t whether AI can automate support, it’s how to make AI work effectively in the complex, human-centric world of enterprise operations. Yesterday, I was on a call with the Senior VP of Operations for a company handling 4 million annual support issues, and the top questions were: 1. How do we test and monitor the AI at scale? What will effective QA from humans look like? 2. What are the guardrails in the model? Will the AI self-QA before the humans have to QA? 3. What's the workflow to manage the knowledge - can the AI go and update our knowledgebase when it learns new topics? 4. How do we design a hybrid support model so that AI<>Humans can collaborate depending on who is best equipped to respond 6. Most importantly, how do you integrate AI agents into complex enterprise systems without disrupting workflows? - Zendesk + Confluence + Notion + Slack These aren’t just technical challenges, they’re operational and strategic challenges that require deep expertise in both AI and customer experience. The future of AI in customer support isn’t just about the models themselves. While foundational AI infrastructure will inevitably become commoditized (Welcome DeepSeek AI), the real value lies in application layer - the tools and systems that bring AI agents to life and deliver real value in the messy, hybrid environments of large enterprises, with minimal changes. At Fini, we’re building the future of AI-driven support by tackling these questions head-on and delivering real value for our enterprise customers. Out platform makes it dead easy for enterprises to self-deploy, and let their CX teams manage AI<>Human collaboration. The future of customer support is here, and it’s hybrid. Let’s build it together.

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