Sergey Ilin’s Post

Choosing the right AI Agent framework can feel overwhelming. Should you go experimental (AutoGPT, BabyAGI) or production-ready (LangChain, Rasa, LlamaIndex)? It really comes down to your primary focus: 🔄 Orchestration 💬 Chatbots 📊 Data & Retrieval The landscape is evolving FAST—agentic AI is no longer just a buzzword; it’s shaping how teams build, deploy, and scale automation. 👉 Which framework are you betting on for 2025? #AI #AgenticAI #RAG #LangChain #AutoGPT #ArtificialIntelligence #FutureOfWork

Reusing everything we’ve built for humans might be the wrong way to design for AI agents. Some days ago, I had a deep conversation with one of our architects at Emplifi about our future AI agent for Analytics. For clarity, an AI agent is a system (nowadays often powered by LLMs) that can take goals expressed in natural language and orchestrate a set of actions (whether that means pulling data, analyzing trends, building a chart, or calling an external tool) to achieve them, often autonomously. At some point, he asked: “How can we build our new Analytics AI Agent reusing all we have built for Analytics so far?” It’s a legit question. Anyone building products wants to move fast and extend the value of existing components. My answer was: “Probably we can't...” Here’s why. When we build for humans, we design to save them from repetitive work, often by creating shortcuts, guardrails, or simplified workflows. People are usually fine with a little upfront learning if the payoff is big efficiency later. For AI, the trade-offs are different. LLM-based agents don’t get tired of repetition. Looping through simple tasks at scale is exactly what they’re good at. Where they struggle is with tasks that require managing ambiguity, context-switching, or handling a very large reasoning space, which is costly and slow. This means some human-centric components (like heavy UI simplifications) may not bring much value to an AI. Instead, AI often benefits from more direct, lower-level access to data and actions, even if that would be overwhelming for a person. So, while parts of our Analytics will transfer directly, others will need new versions optimized for AI rather than humans. In practice, that means designing two layers: one that makes life easier for people, and another that makes orchestration easier for machines (until the two layers eventually merge into one). If you’ve faced similar trade-offs between human and AI design, I’d love to hear your story.

📌 Lessons From My First Read of Building Business-Ready Generative AI Systems 🤔 Why do 80% of agentic AI pilot projects never make it to production? Simple: because most are designed as demos, not as business-ready systems. This Saturday, a book landed on my doorstep. My first thought? “Here we go again… another AI book full of buzzwords?” But then the question hit me: 👉 Could this one actually help solve the problems that I (and so many AI engineers) keep running into when designing enterprise-scale agentic AI systems? So I cracked it open: 📖 Building Business-Ready Generative AI Systems by Denis Rothman. Pretty quickly, I started nodding. Because the same 8 pitfalls I’ve seen in real-world projects were right there on the page (maybe you’ve seen them too): 1️⃣ No memory = no trust → agents forget context, users walk away 2️⃣ Built for 1, not many → single-user demos don’t work in enterprise workflows 3️⃣ Live data chaos → MCP enables access, but without solid design, integrations collapse 4️⃣ Shallow reasoning → agents can chat, but fail at multi-step logic and decisions 5️⃣ Scaling cracks → smooth in the lab, broken under real-world demand 6️⃣ Stuck in silos → great in one domain, blind to the bigger business picture 7️⃣ Text-only trap → ignoring voice, images, and multimodal data limits impact 8️⃣ Security blind spots → without governance, integrations open the door to risk 🚀 The big lesson? An AI Agent isn’t powerful just because it chats. It becomes powerful when it can remember, reason, integrate, automate, and scale securely. And this is just the beginning. The journey ahead looks like this: 🧠 AI Controllers → 🔄 Dynamic RAG → 🎙️ Multimodal reasoning → 💡 Consumer memory agents → 🚚 Trajectory prediction → 🛡️ Security & governance → 💼 Investor-ready design ✨ I’ll keep sharing takeaways as I move through the chapters—so we can explore this journey together. 👉 Which of these pitfalls have you seen most often in AI projects? P.S. Ever built an AI demo that looked brilliant… and then broke the second it hit the “real world”? (Yeah, me too.)

Architect better AI agents with a simple 2×2 (for PMs & AI tinkerers) 💡 At Wordsworth AI, before we start building our agents, we have long discussions on how human computer interaction (HCI) would look like. This has always helped us get to the right answer faster, less engineering-product back and forth. Here’s a simple framework we use to decide what kind of agent/UI to build. Plot your task on two axes: 1. Friction = number of handoffs/steps to reach “done” 🚨 2. Judgment = how much human taste/decision-making is required ⚡ Quadrants (and what to build): 1. Low Judgement, Low Friction → One-shot / Few-shot 🧭 - Template + one-shot/few-shot → deterministic output - Ship as a single-click action or API trigger 2. Low Judgement, High Friction → Assembly line 🏭 - Multi-stage pipeline; human = curator only - Batch actions, pre/post validators, checkpoints 3. High Judgement, Low Friction → Creative canvas 💟 - Generate many variants; diverge → rank → converge - Add quick ranking, pairwise votes, style/taste capture 4. High Judgement, High Friction → Tight HCI loops 🏃➡️ - Break into micro-steps with review gates - Inline edits, “ask/approve” prompts, undo/versioning How to use this today? 👉 1. Map your top agent ideas into the 2×2. 2. Pick the HCI pattern that matches the quadrant (one-shot, curator pipeline, canvas, or tight loop). 3. Create a quick and dirty MVP of the agent using a workflow tool like n8n, Gumloop. 4. Get the output in a spreadsheet, evaluate the output. Iterate for loss paterns. In the next few posts, we will cover the exact tech stack we use to implement these patterns (routing, memory, tools, evals, and the UI bits). If you are curious to learn more about AI agents/ future of work, follow me! And always love to hear other takes on this topic. Pls comment/add thoughts. #AI #agentic #startup #product Harsha Mulchandani Siddhant Manocha Arjav Jain

Generative AI can now produce working code and prototypes in minutes, yet in enterprise settings the real limitation isn’t the models at all, it’s the processes we work within. That gap is where my reflections lie. I’ve put some fuller thoughts into an article and would welcome any reflections from those facing the same challenges.

To build GREAT Agentic AI .... an awesome takeaway I gained from this article... - "Stateless by default: Subagents are pure functions - Clear boundaries: Explicit task definitions and success criteria - Fail fast: Quick failure detection and recovery - Observable execution: Track everything, understand what’s happening - Composable design: Small, focused agents that combine well" Love practitioners and builders sharing reality vs hype. Build on! Best Practices for Building Agentic AI Systems: What Actually Works in Production - UserJot https://smpl.is/abcd0

Building Agentic AI Systems: Lessons from the Trenches I came across a fantastic breakdown by Shayan Taslim at UserJot on what actually works when moving from AI experiments → to production-ready agentic AI systems. Having seen how hard it can be to keep multi-agent systems from collapsing under their own complexity, this resonated deeply. Key principles I pulled out: Two-tier architecture wins – A single primary agent for orchestration + stateless subagents for execution. Anything beyond that becomes debugging chaos. Stateless subagents are non-negotiable – treat them like pure functions: input → output. No memory, no baggage. This makes testing, caching, and scaling far easier. Task decomposition matters – know when to break work vertically (sequential) vs. horizontally (parallel). Parallelization can turn 5-minute jobs into 30-second ones. Explicit communication beats magic – structured task specs in, structured outputs out. Agents are tools, not fortune tellers. Monitor ruthlessly – track success rates, latency, token usage, and failure modes from day one. What stood out most to me: the pitfalls. Overly “smart” agents, creeping state, deep hierarchies, and context overload all seem tempting shortcuts, but they collapse quickly in production. The winning recipe is simplicity, modularity, and clarity. My takeaway: The hype often makes agentic AI sound like magic, but the systems that work in production look surprisingly boring: small, specialized, observable, and built with engineering discipline. That’s what makes them reliable. Source: UserJot — Best Practices for Building Agentic AI Systems: What Actually Works in Production (https://lnkd.in/gdr-Z--c?) #AI #AgenticAI #MLOps #SystemDesign #LLMs #AIEngineering #BestPractices

I’ve always believed the true potential of AI was in reasoning, planning, and acting autonomously. It's a vision I've held for years. But like many in tech, my career focus took me on a different, necessary path. Now, with the incredible advancements in LLMs, it's clear the future I always envisioned is an engineering reality. That's why I'm kicking off a public learning journey to master the core principles of agentic AI architecture. This is about deeply understanding the fundamental concepts required to design, build, and operate production-grade autonomous systems.   I'll be sharing my progress as I master the key concepts in each phase. Phase 1: The Cognitive Architecture of an Agent Before writing a single line of code, I'll focus on the universal building blocks of any AI agent. • Core Concepts: Deconstructing the Profiling, Memory, Planning, and Action modules. Understanding loops like ReAct and Reflection. • Understanding the Ecosystem: Before diving further, I have to make a comparative analysis of the current technologies out there. Phase 2: Multi-Agent Orchestration & Cloud Foundations This phase is about how specialized agents collaborate. • Core Concepts: Exploring collaboration patterns. Phase 3: Applied Agentic Design Patterns Here, I'll start to build a portfolio of projects, each designed to master a specific, high-value architectural pattern. • Linear Workflows: Building an Autonomous Content Strategist. • Secure Web Interaction: Creating a Browser Automation Bot with robust safety guardrails. • Multimodal RAG: Architecting an AI Content Director that analyzes video and audio. Phase 4: Full-Stack System Design for AI This is where we bridge the gap from a script to a real product. •Core Concepts: Architecting for long-running tasks with asynchronous background processing. Implementing serverless deployment models for scalability and cost-efficiency. Phase 5: Production-Grade AgentOps Finally, I'll tackle the critical, non-negotiable disciplines required to run these systems reliably and safely in the real world. Core Concepts: • Observability: Implementing deep tracing to understand and debug probabilistic systems. • Security: Building multi-layered input, output, and process-level guardrails. • Cost Management: Strategies for monitoring, analyzing, and optimizing LLM-related expenses. My goal is to build a deep, first-principles understanding of how to architect autonomous systems, the skills that are essential for building the next generation of software.   I want to share my learnings and connect with others who are passionate about this space. Just to clarify, this is a living plan, as we dive in further we can have any number of things that can change the plan. And that's ok. A video series is soon to out as well. 👇 I want to hear from you! What core concepts or challenges in agentic AI are you most curious about? #AI #AgenticAI #SystemArchitecture #SoftwareEngineering #LLM #TechJourney #AIaaS

Beyond Proof of Concept: Making Generative AI Solutions Production-Ready  Many generative AI projects never make it past the proof-of-concept (POC) stage. The gap between a slick demo and a production-ready solution is often massively underestimated. After working on multiple GenAI initiatives, here are a few tips I’ve learned about designing AI solutions that actually deliver in production: 1️⃣ Start with a real business problem Don’t chase “cool demos.” Anchor your solution to a business-critical problem tied to measurable outcomes (efficiency, accuracy, revenue). 2️⃣ Design for validation from the start GenAI is non-deterministic. Always give business users a way to check the output — whether through citations, context, or confidence scores. 3️⃣ Involve business users early Real feedback > lab assumptions. Get business users into the loop before launch and treat their input as fuel for product evolution — not just bug reports. 4️⃣ Apply the 80/20 rule Prioritize the 20% of features that drive 80% of the value. A simple, reliable solution beats a fragile “feature-complete” one every time. 5️⃣ Build for monitoring + iteration Launch isn’t the finish line. Data shifts, user behavior evolves, and edge cases appear. Bake in monitoring, feedback loops, and retraining processes from day one. 6️⃣ Have responsive AI governance ready Policies and guardrails shouldn’t just exist on paper — they need to adapt as models, regulations, and business use cases evolve. Governance must be agile enough to support innovation and protect against risk. 👉 The key: design for scalability, reliability, and usability from the very beginning. What strategies have you found most effective in taking GenAI from POC to production? #GenerativeAI #AIinProduction #AIProductDevelopment #POCtoProduction

Yes, AI can move fast—but speed isn’t the breakthrough. Context is. The truth is, building with AI isn’t just about writing clever prompts. It’s about designing context the same way we’ve always had to design software systems. Think about it like software development in the old days: --> You still needed architecture. --> You still needed “include files” and libraries. --> You still needed to define the rules before the code made sense. AI is no different. In fact, because it’s faster and smarter, the stakes are higher. If we don’t provide structured context, the output unravels just as quickly as it’s produced. That’s why I’m excited about the idea of Context Engineering—it’s not a gimmick, it’s the new operating system for complex AI work. At the end of the day, context really is king.