Chatbot Scalability Considerations

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?

I am deploying my own LLM Mistral-7B-instruct with supercharged inference As I work on building a chat assistant with Mistral-7B to help customers navigate complex SAAS platform, I run into an important consideration, how will I scale and serve the LLM running the assistant. Let's look at a scenario: Using one GPU-A100 for deployment, our LLM Mistral-7B can generate 17 tokens per second. Now, lets say, if we have 1000 customers using our assistant at the same time, and average length of response from assistant is 150 tokens, putting the numbers together, our assistant will take 2 hours to process requests at anytime. An average reader's speed is 240 words per minute which we should match so our readers don't get bored but with the above setup, more than half the customers could even be waiting 1 hour to get any text at all. Not good at all for User Experience!! First, lets define the metrics we will use to assess the performance of LLM in the context of deployment: - Latency : Total time taken to process one user query. Important for better UX - Throughput: The number of tokens generated per second by the system. Important for scalability We are going to use a popular framework vLLM for optimization and benchmarking but lets look at the basic principles that vLLM leverages: 1. KV caching: - Transformer decoder architecture generates tokens sequentially and to generate a token, it uses all the past generated tokens. For each new token, a key-value vectors are generated which measures the relevance of the token to previous tokens. - So lets say, if we want to predict xth token, we will need KV vectors for 1...(x-1)th tokens, these vectors can be cached instead of regenerating them for every token, leading to time optimization with a memory trade-off. 2. Continuous batching our main optimization: - We parallelly process batches of customer queries, enhancing throughput. - However, differing response sizes in generative text lead to inefficient GPU memory use. - For examples: lets create a batch of two queries: - 'Delhi is the capital of which country?' -'Tell me about Harry potter' The first requires a brief response, while the second could be lengthy. With equal memory allocation per query, the GPU waits for the longer response to complete, leading to underutilized memory for the shorter query. This results in a hold-up of memory resources that could have been used for processing other queries. vLLM allows the efficient use of GPU memory to cache KV vectors, such that when a query in a batch is finished, another query can start processing in that batch. Observations on using vLLM on a batch of 60 queries: 1. Latency decreased more than 15x with vLLM 2. Throughput increased from 18 tokens/s to 385 tk/s 3. Throughput shows significant boost on large batches Link to reproduce results on colab: https://lnkd.in/ew_S_2WD If you are working on a similar project, you are welcome to share your experience :)

Your chatbot is just a dumb UI for expensive LLM calls (cost + latency). Most RAG implementations are search wrappers duct-taped to a friendly frontend. They retrieve verbose context, toss it at an LLM and pray it says something smart. It’s not reasoning. It’s autocomplete with citations. Why RAG alone doesn’t scale: - Every query hits your vector DB and foundation model. Latency stacks. Cost climbs. - Retrieval quality plateaus. Chunking strategies don’t solve semantic gaps or reasoning chains. - Context windows aren’t memory. They don’t help the system "understand" what just happened or what’s next. What we actually need: architectures that think. Thinking = state + planning + tool use + memory. A real system architecture should include: 1. Task-oriented planning: Break inputs into intent + sub-tasks. Use small orchestrators (e.g., LangGraph or custom DAGs) to route intelligently. Not everything needs a 30B model hit. 2. Tool selection: Use a model not just to answer but to decide which tool (search, calculator, SQL agent, document parser) fits the task. Build tool-use APIs explicitly, monitor performance per tool. 3. Episodic memory: Cache intermediate steps. Store reasoning chains (not just outputs) in memory slots tied to users or sessions. Use local embedding stores + Redis to track context evolution. 4. Response reconstruction: Use a lightweight final model or template engine to stitch outputs, minimizing latency at the edge. The LLM should generate minimal "glue", not the whole answer blob. We tested these ideas in real production flows. Result? Fewer model calls. Smarter outputs. Lower costs. Faster UX. Stop scaling hallucinations with GPUs. Architect systems that can think. Start with: - Planning nodes - Explicit tools - Lightweight memory - Modular response generation LLMs aren’t apps. They’re reasoning engines. Treat them that way. #GenerativeAI #AIArchitecture #AgenticAI #EnterpriseAI #AIEngineering

𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐟𝐨𝐫 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 Building AI Agents That Scale Isn’t Just About LLMs — It’s About Architecture. If you’re just plugging a model into a chatbot, you’re missing the bigger picture. This 7-step roadmap lays out how to design scalable, capable AI agents that go beyond simple prompt-response patterns — moving toward autonomy, memory, and collaboration: 1️⃣ 𝑷𝒊𝒄𝒌 𝒂𝒏 𝑳𝑳𝑴 Choose models that support reasoning and stability. (LLaMA, Claude, Mistral are great starting points.) 2️⃣ 𝑩𝒖𝒊𝒍𝒅 𝑨𝒈𝒆𝒏𝒕'𝒔 𝑳𝒐𝒈𝒊𝒄 Define how your agent should think: Should it reflect before responding? Plan actions? Use tools? 3️⃣ 𝑾𝒓𝒊𝒕𝒆 𝒊𝒕𝒔 𝑪𝒍𝒆𝒂𝒓 𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑰𝒏𝒔𝒕𝒓𝒖𝒄𝒕𝒊𝒐𝒏𝒔 Craft reusable templates for consistency, especially when interfacing with APIs or tools. 4️⃣ 𝑨𝒅𝒅 𝑴𝒆𝒎𝒐𝒓𝒚 Use sliding window techniques for short-term recall, and vector databases for long-term memory (ZepAI, MemGPT). 5️⃣ 𝑪𝒐𝒏𝒏𝒆𝒄𝒕 𝑻𝒐𝒐𝒍𝒔 & 𝑨𝑷𝑰𝒔 Agents shouldn’t just talk — they should act. Connect them to search, databases, CRMs, etc. 6️⃣ 𝑮𝒊𝒗𝒆 𝑰𝒕 𝒂 𝑱𝒐𝒃 "Be helpful" isn't enough. Narrow scopes like “summarize insights” or “respond with markdown” improve outcomes. 7️⃣ 𝑺𝒄𝒂𝒍𝒆 𝒕𝒐 𝑴𝒖𝒍𝒕𝒊-𝑨𝒈𝒆𝒏𝒕 𝑻𝒆𝒂𝒎𝒔 Distribute responsibilities: one agent for planning, another for execution, a third for QA. 📌 Pro Tip: Use task-specific naming conventions and orchestration frameworks like LangGraph or CrewAI to manage your agent network efficiently. 🔖 Save this as a quick reference! 𝑾𝒂𝒏𝒕 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆? 𝘍𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dTK-FtG3 Follow Shreya Khandelwal for more such content. ************************************************************************ #LargeLanguageModels #ArtificialIntelligence #GenerativeAI #LLM #MachineLearning #AI #DataScience #AIagents #AgenticAI #LangChain #MultiAgent #PromptEngineering #OpenAI