How to Build Reliable LLM Systems for Production

If you’re building AI agents that need to work reliably in production, not just in demos, this is the full-stack setup I’ve found useful From routing to memory, planning to monitoring, here’s how the stack breaks down 👇 🧠 Agent Orchestration → Agent Router handles load balancing using consistent hashing, so tasks always go to the right agent → Task Planner uses HTN (Hierarchical Task Network) and MCTS to break big problems into smaller ones and optimize execution order → Memory Manager stores both episodic and semantic memory, with vector search to retrieve relevant past experiences → Tool Registry keeps track of what tools the agent can use and runs them in sandboxed environments with schema validation ⚙️ Agent Runtime → LLM Engine runs models with optimizations like FP8 quantization, speculative decoding (which speeds things up), and key-value caching → Function Calls are run asynchronously, with retry logic and schema validation to prevent invalid requests → Vector Store supports hybrid retrieval using ChromaDB and Qdrant, plus FAISS for fast similarity search → State Management lets agents recover from failures by saving checkpoints in Redis or S3 🧱 Infrastructure → Kubernetes auto-scales agents based on usage, including GPU-aware scheduling → Monitoring uses OpenTelemetry, Prometheus, and Grafana to track what agents are doing and detect anomalies → Message Queue (Kafka + Redis Streams) helps route tasks with prioritization and fallback handling → Storage uses PostgreSQL for metadata and S3 for storing large data, with encryption and backups enabled 🔁 Execution Flow Every agent follows this basic loop → Reason (analyze the context) → Act (use the right tool or function) → Observe (check the result) → Reflect (store it in memory for next time) Why this matters → Without a good memory system, agents forget everything between steps → Without planning, tasks get run in the wrong order, or not at all → Without proper observability, you can’t tell what’s working or why it failed → And without the right infrastructure, the whole thing breaks when usage scales If you’re building something similar, would love to hear how you’re thinking about memory, planning, or runtime optimization 〰️〰️〰️〰️ ♻️ Repost this so other AI Engineers can see it! 🔔Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources 📙I write long-form technical blogs on substack, if you'd like deeper dives: https://lnkd.in/dpBNr6Jg

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

Check out this framework for building AI Agents that work in production. There are many recommendations out there, so would like your feedback on this one. This is beyond picking a fancy model or plugging in an API. To build a reliable AI agent, you need a well-structured, end-to-end system with safety, memory, and reasoning at its core. Here’s the breakdown: 1.🔸Define the Purpose & KPIs Start with clarity. What tasks should the agent handle? Align goals with KPIs like accuracy, cost, and latency. 2.🔸Choose the Right Tech Stack Pick your tools: language, LLM, frameworks, and databases. Secure secrets early and plan for production-readiness from day one. 3.🔸Project Setup & Dev Practices Structure repos for modularity. Add version control, test cases, code linting, and cost-efficient development practices. 4.🔸Integrate Data Sources & APIs Link your agent with whatever data it needs to take action intelligently from PDFs, Notion, databases, or business tools. 5.🔸Build Memory & RAG Index knowledge and implement semantic search. Let your agent recall facts, documents, and links with citation-first answers. 6.🔸Tools, Reasoning & Control Loops Empower the agent with tools and decision-making logic. Include retries, validations, and feedback-based learning. 7.🔸Safety, Governance & Policies Filter harmful outputs, monitor for sensitive data, and build an escalation path for edge cases and PII risks. 8.🔸Evaluate, Monitor & Improve Use golden test sets and real user data to monitor performance, track regressions, and improve accuracy over time. 9.🔸Deploy, Scale & Operate Containerize, canary-test, and track usage. Monitor cost, performance, and reliability as your agent scales in production. Real AI agents are engineered step by step. Hope this guide gives you the needed blueprint to build with confidence. #AIAgents

I taught myself how to build AI agents from scratch Now I help companies deploy production-grade systems These are my favorite resources to set you up on the same path: (1) Pick the Right LLM Choose a model with strong reasoning, reliable step-by-step thinking, and consistent outputs → Claude Opus, Llama, and Mistral are great starting points, especially if you want open weights. (2) Design the Agent’s Logic Decide how your agent thinks: should it reflect before acting, or respond instantly?How does it recover when stuck? → Start with ReAct or Plan–then–Execute: simple, proven, and extensible. Start with ReAct or Plan–then–Execute (3) Write Operating Instructions Define how the agent should reason, when to invoke tools, and how to format its responses. → Use modular prompt templates: they give you precise control and scale effortlessly across tasks. (4) Add Memory Your agent needs continuity — not just intelligence. → Use structured memory (summaries, sliding windows, or tools like MemGPT/ZepAI) to retain what matters and avoid repeating itself. (5) Connect Tools & APIs An agent that can’t do anything is just fancy autocomplete. → Wire it up to real tools and APIs and give it clear instructions on when and why to use them. (6) Give It a Job Vague goals lead to vague results. → Define the task with precision. A well-scoped prompt beats general intelligence every time. (7) Scale to Multi-Agent Systems The smartest systems act an ensembles. → Break work into roles: researcher, analyst, formatter. Each agent should do one thing really well. The uncomfortable truth? Builders ship simple agents that work. Dreamers architect complex systems that don't. Start with step 1. Ship something ugly. Make it better tomorrow. What's stopping you from building your first agent today? Repost if you're done waiting for the "perfect" agent framework ♻️ Image Credits – AI Agents power combo: Andreas Horn & Rakesh Gohel

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

Everybody wants to talk about using AI Agents, but how many understand what it takes to truly build and maintain them? AI Agents, like any ML model, requires monitoring post-deployment. But AI Agents are different than traditional AI models in that many industry AI Agents are built using APIs trained by third party companies. This means monitoring both during and after deployment is critical. You'll need to monitor things like usage relative to the rate limit of the API, latency, token usage, and how many LLM calls your AI Agent makes before responding. You'll even need to monitor failure points at the API level as bottlenecking and region availability can bring your entire AI solution down. Tools like Splunk, DataDog, and AWS CloudWatch work well here. They help you track these metrics and set up alerts to catch issues before it affects your AI Agent build. LLM usage costs take far too many companies by surprise at the end of a POC. Don't be that company. Monitor closely, set thresholds, and stay on top of your AI Agent's performance and costs.

LLM agents are too expensive and too unreliable. Unfortunately, building agentic workflows that work beyond a good demo is hard. I talk daily to people who are trying, and it's tough. I have a paper and a few experiments to show you. A solution that cuts the costs of running an AI assistant by up to 77.8%. This is a game-changer for the future of agents! Just so we are on the same page, here is the most popular approach to building agentic workflows: Write a long system prompt that provides instructions to the LLM on how to answer users' queries. Tell the model how to react to different situations and the business logic it should follow to create its answers. This is simple to build, extremely flexible, and completely unreliable. One minute, it works like magic. The next, you get garbage results. No serious company will ever use this. A paper published earlier this year proposes a much more structured strategy. Its focus is on finding a middle ground between the flexibility of an LLM and reliable responses. The 10-second summary: Instead of the one-prompt-to-rule-it-all approach, this new strategy separates the business logic execution from the LLM's conversation ability. • This leads to cheaper agents (77.8% is a big deal) • Much higher consistency in following rules • More reliable responses Here is a blog post that goes into much more details about how this works and a few experiments: https://hubs.ly/Q02MQCQh0 Look at the attached image: that's the difference in cost and latency between the new approach, and a more traditional agent. You'll find the link to the paper in the image ALT description. To reproduce the experiments, check out this GitHub repository: https://lnkd.in/gydzjjcu

Customer-facing AI agents keep failing in production...🤯 Because existing agent frameworks lack some fundamental features. I've spent months building with every major AI agent framework and discovered why most customer-facing deployments crash and burn: → Flowchart builders (Botpress, LangFlow) create rigid paths that customers often break → System prompt frameworks (LangGraph, AutoGPT) excel in demos but fail due to AI's unpredictability Parlant's opensource Conversation Modeling Engine solves this. Here's how and why it matters: 1. Contextual Guidelines vs. Rigid Paths ↳ Instead of mapping every possible conversation flow, define what your agent should do in specific situations. ↳ Each guideline has a condition and an action - when X happens, do Y. ↳ The engine matches only relevant guidelines to each customer message. 2. Guided Tool Use That Stays Reliable ↳ Tools are tied directly to specific guidelines. ↳ No more random API calls or hallucinated data. ↳ Your travel agent won't suddenly search flights when someone asks about baggage fees. 3. Priority Relationships for Natural Conversation ↳ Guidelines have relationships with each other. ↳ When multiple guidelines match, the engine selects based on priority. ↳ Creates step-by-step information gathering without rigid flowcharts. 4. The "Utterances" Feature for Regulated Industries ↳ Pre-approve specific responses for sensitive situations. ↳ Agent checks if an appropriate Utterance exists before generating. ↳ Completely eliminates hallucinations in critical interactions. It works with any major LLM provider - OpenAI, Anthropic, Google, Meta. This approach handles what flowcharts and system prompts can't: The messy reality of actual customer conversations. Your IP isn't the LLM. It's the conversation model you create. The explicit encoding of how your AI agent should interact with customers. For anyone building agents that need to stay reliable in production, this might be the framework you've been waiting for. Check it out: https://lnkd.in/dNPSDJ7P P.S. I create AI Agent tutorials and opensource them for free. Your 👍 like and ♻️ repost helps keep me going. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.