How to Design AI Workflows

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?

I build AI agents for a living and after auditing 100+ AI agent systems and studying the latest agent playbooks from OpenAI, Google, and Anthropic... Here’s the simplest, clearest guide I’ve found for building real agents — the kind that think, act, and adapt like a team member, not a chatbot. 🧠 What’s an AI Agent? An agent is a system that: ⨠ Uses an LLM/Reasoning model to understand and reason ⨠ Can take action (via tools/functions/APIs) ⨠ Maintains memory and multi-step context ⨠ Operates within goal-driven logic ⨠ And self-corrects when things go wrong Not just respond. Act. Decide. Adapt. The 5 Components of Any Real Agent (All 3 Playbooks Agree) 🧠 Model (LLM) → Powers reasoning and planning (OpenAI, Claude, Gemini) → Use different models for different steps (cost × latency × complexity) 🔧 Tools (or APIs) → Extend the agent beyond knowledge — into execution → Can be action APIs (send email), retrieval (RAG), or data access (SQL, PDFs) 🧭 Orchestration Layer → Loop that plans > acts > adjusts → Uses frameworks like ReAct, Chain-of-Thought, or Tree-of-Thoughts 🛡️ Guardrails → Input filtering, safety checks, escalation logic → Think: “When do we bring in a human?” 🧠 Memory / State → To handle multi-step workflows, learn over time, and recover from errors 🚀 Want to Build? Start Here: ⨠ Pick 1 task with high cognitive load (not high risk) ⨠ Define the goal, success condition, and edge cases ⨠ Give the agent 1 tool and 1 model ⨠ Add logic: “If [X], do [Y]. Else escalate.” ⨠ Test 10 cases. Break it. Refine. ⚡ Pro Tip: Use This Prompt Stack “You’re an expert AI architect. Design a simple agent that completes [goal] using only 1 model, 1 tool, and clear exit logic.” “Add fallback logic if the agent fails or gets stuck.” “Define 5 test cases to validate it.” “Now output this as a visual workflow + API schema.” We don’t need more copilots. We need real agents — that can reason, act, and learn in real time. This is how you build one. — 📥 Want the full Agent Playbook (Google x Anthropic x OpenAI)? ⨠ Comment “AGENT”, connect with me, and I’ll DM you the full playbook. Because in 2025, knowing how to talk to AI isn’t enough. You need to know how to hire, train, and deploy it. ______________________________________________________________ I’m Amit. I help ambitious thinkers and founders design their lives like systems — using AI to work smarter, live longer, and grow richer with clarity and calm. Missed my last drop? ⨠ How o3 is a game changer https://lnkd.in/dQ3Q8s7C? ♻️ Repost to help someone think better today. ➕ Follow Amit Rawal for AI tools, clarity rituals, and high-agency systems.

Want to Build an AI Agent? Here's a short guide ↓ 𝟭. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 Before you even touch the code, clarify: ⤷ What problem is it solving? ⤷ What type of inputs/outputs will it handle? ⤷ Does it need real-time capabilities or just batch processing? Example: A legal document assistant that summarizes contracts. 𝟮. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝗹 You have options given cost + latency + performance: ⤷ Pre-trained LLMs (e.g., GPT-4, Claude) for general tasks. ⤷ Open-source (ie. Llama, Deepseek) ⤷ Fine-tuned models for specific tasks Example: A customer support chatbot might use GPT-4 with a vector database for company-specific FAQs. 𝟯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Think of your agent like a pipeline: ⤷ Input Handling (user query, API calls) ⤷ Processing (retrieving data with RAG, using tools) ⤷ Output Generation (text response, action execution) For complex tasks, consider multi-agent systems, where different agents handle subtasks. 𝟰. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Most AI agents need to recall past interactions. Use: Short-term memory (conversation history) Long-term memory (knowledge base or vector store) Example: A personal AI tutor remembering a student’s past mistakes. 𝟱. 𝗗𝗲𝗽𝗹𝗼𝘆 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 Choose how you’ll deploy: ⤷ Cloud-based (e.g., AWS, Azure, GCP) ⤷ Edge AI for privacy-sensitive tasks. Continuously monitor agent performance, latency, cost with tools like AgentOps, Langfuse, LangSmith. Iterate and optimize. ⤷ What else is vital in building an agent? Drop one ↓ 

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace

Agents will unlock the next wave of productivity gains for the enterprise...but they also have their own unique set of operational challenges Let's check the lifecycle for AI Agentic development 𝗗𝗲𝘀𝗶𝗴𝗻: 1. Define agent use case, detailed workflow and KPIs to align with business goal. 2. Identify data sources (tools) available to validate feasibility of project. 3. Select/fine-tune appropriate model to suit the agentic workflow. 4. Define appropriate architecture & patterns (framework & libraries) to enable reasoning, planning, self-improvement, tool usage. 5. Design underlying infrastructure to optimize cost-effectiveness. 𝗕𝘂𝗶𝗹𝗱 & 𝗗𝗲𝗽𝗹𝗼𝘆: 1. Integrate agentic workflow with LLM inference provider. 2. Integrate service with data sources (tools) across environments. 3. Simulate and debug service behavior. Guardrail actions and outputs. 𝗖𝗼𝗻𝘀𝘂𝗺𝗲 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿: 1. Deploy agentic workflow as API endpoint. Ensure access control and security. 2. Integrate agentic workflow with application services (UI, etc.). 3. Monitor agentic workflow KPIs & logs to ensure optimized results, provide transparency & explainability. AI agents need supporting enterprise capabilities to overcome adoption barriers and be deployed at scale.

You don’t need to be an AI agent to be agentic. No, that’s not an inspirational poster. It’s my research takeaway for how companies should build AI into their business. Agents are the equivalent of a self-driving Ferrari that keeps driving itself into the wall. It looks and sounds cool, but there is a better use for your money. AI workflows offer a more predictable and reliable way to sound super cool while also yielding practical results. Anthropic defines both agents and workflows as agentic systems, specifically in this way: 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: systems where predefined code paths orchestrate the use of LLMs and tools 𝗔𝗴𝗲𝗻𝘁𝘀: systems where LLMs dynamically decide their own path and tool uses For any organization leaning into Agentic AI, don’t start with agents. You will just overcomplicate the solution. Instead, try these workflows from Anthropic’s guide to effectively building AI agents: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁-𝗰𝗵𝗮𝗶𝗻𝗶𝗻𝗴:  The type A of workflows, this breaks a task down into sequential tasks organized and logical steps, with each step building on the last. It can include gates where you can verify the information before going through the entire process. 𝟮. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: The multi-tasker workflow, this separates tasks across multiple LLMs and then combines the outputs. This is great for speed, but also collects multiple perspectives from different LLMs to increase confidence in the results. 𝟯. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: The task master of workflows, this breaks down complex tasks into different categories and assigns those to specialized LLMs that are best suited for the task. Just like you don’t want to give an advanced task to an intern or a basic task to a senior employee, this find the right LLM for the right job. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝘄𝗼𝗿𝗸𝗲𝗿𝘀: The middle manager of the workflows, this has an LLM that breaks down the tasks and delegates them to other LLMs, then synthesizes their results. This is best suited for complex tasks where you don’t quite know what subtasks are going to be needed. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: The peer review of workflows, this uses an LLM to generate a response while another LLM evaluates and provides feedback in a loop until it passes muster. View my full write-up here: https://lnkd.in/eZXdRrxz