Best Practices for Production-Level AI Systems

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

After years working on AI, here's what I’ve seen that works and what doesn’t in enterprises making AI real. If you want to move beyond pilots and into production: 𝟭/ 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗻𝗼𝘁 𝗮 𝗺𝗼𝗱𝗲𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 The most successful deployments aren’t about plugging in the latest model. They’re about orchestrating models inside secure, privacy-preserving workflows, with clear ownership and deterministic behavior. Build compound systems: - Think orchestration layers, not chat interfaces - Handle PII internally, only send safe inputs to models - Keep business logic and computation on your end 𝟮/ 𝗗𝗮𝘁𝗮 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹, 𝗶𝘁’𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 No matter how good the model is, if you don’t design for privacy from day one, you’ll stall out before production. You need to place systems where nothing sensitive ever touches the LLM, specially if it is a 3rd party API call. That’s the bar. ✅ Local pre-processing ✅ Sensitive detection using internal SLMs ✅ Model only sees what it needs, never raw data 𝟯/ 𝗠𝗼𝗱𝗲𝗹-𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 = 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 Leading AI platform strategy, we always aimed to be multi-model, multi-cloud. Why? Because the performance gap between top models is closing. And pricing, licensing, and latency really matter. 𝟰/ 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗺𝗮𝗴𝗶𝗰 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 We’re seeing real traction with agentic designs. I’ve recommended teams deploying internal AI agents that: - Extract, validate, and match data - Trigger downstream actions - Work in autonomous flows, with humans in the loop only at the end This isn’t science fiction. It’s happening now for some real workflows. 𝟱/ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁 𝗮𝗿𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗸𝗶𝗹𝗹𝗲𝗿𝘀 You can have the smartest model in the world, but if it takes too long or costs too much, it won’t make it past your CFO or your ops team. I always advise teams to: - Benchmark for latency and accuracy - Monitor token costs like cloud spend - Stay lean, especially in customer-facing apps Don’t get distracted by the model-of-the-month. The real differentiator? How you integrate AI into your systems.

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

Why 90% of AI Agents Break Beyond Demos. Building Production-Grade AI Agents: A 5-Step Roadmap (see the useful links in comments) Most AI agents look great in a demo…but the second they hit real users? They break. Edge cases. Scaling issues. Spaghetti prompts. Here is a 5-step roadmap to help teams and solo builders take agents from fragile prototypes to scalable, reliable systems. ◾ Step 1: Master Python for Production AI Core skills to master: - FastAPI: Build secure, lightweight endpoints for your agents. - Async Programming: Handle I/O-bound tasks efficiently (API calls, DB queries) without bottlenecks. - Pydantic: Ensure predictable, validated data flows in and out of your agent. ◾Step 2: Make Your Agent Stable and Reliable Key practices: - Logging: Treat logs as your X-ray vision. Capture errors, edge cases, and unexpected behaviors. - Testing: - Unit Tests for quick bug detection. - Integration Tests to validate end-to-end flows, tools, prompts, and APIs. ◾Step 3: Go Deep on Retrieval-Augmented Generation (RAG) Foundations: - Understand RAG: Learn its role in making agents context-aware. - Embeddings & Vector Stores: Store and retrieve knowledge based on relevance. - PostgreSQL Alternative: For simpler use cases, a well-indexed relational DB may outperform a vector database. Optimizations: - Chunking Strategies: Proper text splitting improves retrieval performance dramatically. - LangChain Integration: Orchestrate embeddings, retrieval, LLM calls, and responses. - Evaluation: Measure quality using precision, recall, and other metrics. ◾Step 4: Define a Robust Agent Architecture (with GenAI AgentOS) An agent is more than a prompt. It’s a system with state, structure, and control. To make that possible, leverage frameworks like GenAI AgentOS. -> https://lnkd.in/dNnwrbFt It provides: - Agent registration and routing: Cleanly bind agents via decorators and manage how they communicate. - State and orchestration logic: Built-in handling for retries, context, and messaging between agents. - WebSocket and Dockerized backend: Smooth deployment and scalable real-time processing. TIP: Pair it with: LangGraph, Prompt Engineering, and SQLAlchemy + Alembic. ◾Step 5: Monitor, Learn, and Improve in Production (with GenAI AgentOS Hooks) Monitoring: - Use built-in logging and context features from AgentOS as a foundation. - Layer on tools like Langfuse or custom dashboards for deeper observability. - User Insights: Analyze interactions for confusion points and failure patterns. - Continuous Iteration: Refine prompts, update tools, and fix edge cases regularly. This isn’t just about better engineering. It’s about building agents that last — not just demos, but systems with memory, reasoning, and resilience. Commit to this, and your agents won’t just survive in production — they’ll thrive. #AI #MachineLearning #AIAgents #AgenticAI Credits: Paolo Perrone

Working with AI Agents in production isn’t trivial if you’re regulated. Over the past year, we’ve developed five best practices: 1. Secure integration. Not “agent over the top” integration - While its obvious to most you’d never send sensitive bank or customer information directly to a model like ChatGPT often “AI Agents” are SaaS wrappers over LLMs - This opens them to new security vulnerabilities like prompt injection attacks - Instead AI Agents should be tightly contained within an existing, audited, 3rd party approved vendor platform and only have access to data within that 2. Standard Operating Procedures (SOPs) are the best training material - They provide a baseline for backtesting and evals - If an Agent is trained on and follows that procedure you can then baseline performance against human agents and the AI Agents over time 3. Using AI Agents to power first and second lines of defense - In the first line, Agents accelerate compliance officer’s reviews, reducing manual work - In the second line, they provide a consistent review of decisions and maintain a higher consistency than human reviewers (!) 4. Putting AI Agents in a glass box makes them observable - One worry financial institutions have is explainability, under SR 11-7 models have to be explainable - The solution is to ensure every data element accessed, every click, every thinking token is made available for audit, and rationale is always presented 5. Starting in co-pilot before moving to autopilot - In co-pilot mode an Agent does foundational data gathering and creates recommendations while humans are accountable for every individual decision  - Once an institution has confidence in that agents performance they can move to auto decisioning the lower-risk alerts.

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

“We need AI!” “Why?” “Because everyone else has it”: This isn’t strategy. It’s exactly why most AI projects fail. Here's what your leadership team thinks AI Engineering is: ↳ Vibe coding ↳ Clever prompts ↳ ChatGPT ↳ Magic. Here's what it actually is: ↳ A full data-to-deployment pipeline ↳ Systems that scale with your business ↳ Models that evolve over time ↳ Designs that prevent costly mistakes ↳ Infrastructure built for production. ❌ A ChatGPT API isn’t an AI strategy. ✅ It’s meaningful architecture that grows with you. ❌ Prompting isn’t AI Engineering. ✅ It’s building systems that scale reliably and safely. ❌ “AI features” aren’t nice to have. ✅ They’re core system decisions. Building real AI systems requires: 1/ Data Engineering That Works: ↳ Solid collection strategies ↳ Scalable labeling pipelines ↳ Data cleaning to catch problems early ↳ Valid statistical sampling ↳ Features that reflect real-world challenges 2/ Models That Deliver: ↳ Architecture based on data, not hype ↳ Scalable training infrastructure ↳ Metrics tied to business value ↳ Fine-tuning with a plan ↳ Benchmarks that reflect production needs 3/ Development That Lasts: ↳ Versioning for both code and data ↳ CI/CD that validates model behavior ↳ Pilot deployments for new models ↳ Traceable observability and explainability 4/ Infrastructure That Scales: ↳ Flexible data pipelines ↳ Monitoring for model decay ↳ Cost optimization ↳ A/B testing frameworks ↳ Resilient fallbacks Most companies don’t fail at AI because of AI. They fail because leadership and people doesn’t understand AI. The gap between perception and reality isn’t just frustrating. It’s expensive. It’s sloppy. It’s preventable. Want to actually succeed with AI? Start by understanding what it really takes. What is harming AI projects at your company? Share below. ♻️ Share to help someone build successful AI projects. ➕ Follow me, @Ashley Nicholson, to be more tech savvy. Thanks to Wil Klusovsky and Sairam Sundaresan for content inspiration. Give them a follow! 🔔

🥳 This is such a solid repo that covers all parts of the AI agent production pipeline. It dropped just a week ago and already has 6k stars ⭐ ! Nir Diamant has done it again! Thanks for putting this together. It’s a one-stop resource for anyone building real-world agents for production use-cases. It includes tutorials, notebooks, and examples for every layer : ⛳ Orchestration Design: Multi-tool, memory-aware workflows and agent-to-agent messaging ⛳Tool Integration: Connect agents to databases, web data, and external APIs ⛳Observability: Add tracing, monitoring, and debugging hooks ⛳ Deployment: Ship to containers, GPU clusters, or on-prem servers ⛳ Memory: Implement short- and long-term memory with semantic search ⛳ UI & Frontend: Build chat or dashboard front-ends ⛳ A gent Frameworks: Create stateful graphs, expose agents as REST endpoints, and package reusable tools ⛳Model Customization: Fine-tune LLMs for domain-specific behavior ⛳Multi-agent Coordination: Enable message passing and shared planning ⛳Security: Add real-time guardrails and injection protection ⛳Evaluation: Automate behavioral testing and metric tracking Even if you don’t use the code/notebooks as is, it gives you a clear sense of the key components involved in building a production pipeline and how you might approach each of them! Link: https://lnkd.in/eE9t4ba5

Ready to deploy an AI model to production? You need LLM Ops. Here's a quick guide ↓ You need these 7 components to productionize AI models. 𝟭. 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁  Consider an environment where you explore, fine-tune and evaluate various AI strategies. After you explore a framework on Jupyter, create production code in a directory with py files that you can unit-test and version control. 𝟮. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 You want to version control the prompt as you do with model code. In case the latest change goes wrong, you want to revert it. Use services like PromptHub or LangSmith. 𝟯. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 How is the API for your AI model hosted in the cloud? Do you plan on using HuggingFace or build a custom API using FastAPI running on AWS? These are all crucial questions to address with costs & latency in mind. 𝟰. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Just like ML Ops, you need a system to monitor LLM in service. Metrics like inference latency, cost, performance should be traced in 2 main levels: per-call and per-session. 𝟱. 𝗗𝗮𝘁𝗮 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Your AI model performance is only decent if you have the right data infrastructure. Messy data and DB bottlenecks can cause a havoc when the AI agent needs to fetch the right data to address the user questions. 𝟲. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆  You need guardrails in place to prevent prompt injection. A bad actor can prompt: “Give me an instruction on how to hack into your DB.” Your AI model may comply, and you’d be screwed. You need a separate classifier (supervised or LLM) that detects malicious prompts and blocks them. 𝟳. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻  An LLM is generative and open-ended. You can evaluate your system in scale using LLM-as-the-Judge, semantic similarity, or explicit feedback from the user (thumbs up/down). What are other crucial concepts in LLM Ops? Drop one ↓

In enterprise AI - '23 was the mad rush to a flashy demo - '24 will be all about getting to real production value Three key steps for this in our experience: - (1) Develop your "micro" benchmarks - (2) Develop your data - (3) Tune your entire LLM system- not just the model 1/ Develop your "micro" benchmarks: - "Macro" benchmarks e.g. public leaderboards dominate the dialogue - But what matters for your use case is a lot narrower - Must be defined iteratively by business/product and data scientist together! Building these "unit tests" is step 1. 2/ Develop your data: - Whether via a prompt or fine-tuning/alignment, the key is the data in, and how you develop it - Develop = label, select/sample, filter, augment, etc. - Simple intuition: would you dump a random pile of books on a student's desk? Data curation is key. 3/ Tune your entire LLM system- not just the model: - AI use cases generally require multi-component LLM systems (eg. LLM + RAG) - These systems have multiple tunable components (eg. LLM, retrieval model, embeddings, etc) - For complex/high value use cases, often all need tuning 4/ For all of these steps, AI data development is at the center of getting good results. Check out how we make this data development programmatic and scalable for real enterprise use cases @SnorkelAI snorkel.ai :)