Tomáš Korčák’s Post

🚨 Your AI Agent Isn’t Failing Because of the Model. It’s Failing Because of the Harness. Most people think building AI agents is about: • Choosing the right model • Writing better prompts • Adding a few tools But here’s the uncomfortable truth: 👉 That’s not where the real problem is. 📘 The real system is not the model. It’s the “Agent Harness.” The harness is everything around the model: • Orchestration loop • Memory • Context management • Tool execution • Error handling • Guardrails • State & persistence 👉 In simple terms: The model is the brain. The harness is the operating system. 💥 And this is where most agents break: • They forget what happened 3 steps ago • Tool calls fail silently • Context gets polluted • Multi-step tasks collapse midway Not because the model is weak— but because the infrastructure around it is fragile 🔍 Key realization: Two products using the same model can perform completely differently based purely on their harness design 💡 What separates demo agents from production systems: It’s not prompts. It’s: • Context engineering • Verification loops • Memory architecture • Error recovery • Controlled tool exposure 👉 This is where real engineering begins. And here’s the shift happening: We are moving from: Model-centric thinking To: System-centric (harness-centric) AI design 🔥 Final takeaway: If your AI agent fails, don’t ask: “Is the model good enough?” Ask: “Is the system around the model designed properly?” 💬 Curious—are you focusing more on model performance, or on the infrastructure (harness) that actually makes agents work? #AI #AgenticAI #AIGovernance #GenAI #AIArchitecture #LLMOps #AIEngineering #Startups #TechLeadership

Most companies treating AI as a vendor relationship are building on sand. Mistral just launched Forge — a platform that lets enterprises train frontier-grade models directly on their own data. Pre-training on internal datasets, reinforcement learning aligned to company-specific policies, mixture-of-experts architectures for large-scale deployment. ASML, the European Space Agency, Ericsson. These aren't scrappy startups experimenting with prompts. They're encoding decades of proprietary knowledge — terminology, workflows, compliance requirements — directly into model behavior. Here's the uncomfortable truth: the companies winning with AI right now aren't winning because they picked a better API. They're winning because they turned their internal data into a model that no competitor can replicate. Generic public models are a commodity. Every company using the same GPT wrapper is competing on execution alone, and that's a brutal place to be. The real moat has always been what you know that others don't. The only question is whether you've structured that knowledge in a way a model can learn from it. Three years from now, the companies that treated AI like a SaaS subscription will look a lot like companies that outsourced their entire engineering team. What's stopping your company from treating proprietary data as a model training asset rather than just a database? #AI #EnterpriseAI #MachineLearning #Startups #ArtificialIntelligence #TechLeadership #LLM Join Agentic Engineering Club → t.me/villson_hub

From PoC to Production: Scaling AI the Right Way In the last 18 months, I’ve had countless conversations with engineering leaders facing the same challenge. They’ve built impressive LLM-powered prototypes. Internal demos work flawlessly. Stakeholders are excited. But the moment the question shifts to: 👉 “Can this handle 50,000 real users?” Silence. The reality? A Proof of Concept (PoC) proves the math. Production proves the engineering. Today, the industry is filled with brilliant AI PoCs that never made it to production. Not because the models failed—but because the systems around them weren’t ready. Scaling AI isn’t just about better prompts or bigger models. It’s about systems thinking and operational excellence. Here’s what truly matters: 🔹 Reliability over novelty – Can your system handle failures gracefully? 🔹 Scalability by design – Architecture must grow with demand, not break under it 🔹 Observability – You can’t scale what you can’t measure 🔹 Cost control – LLM usage at scale is an engineering problem, not just a budget line 🔹 Security & governance – Especially when dealing with real user data If you treat AI like a science experiment, it stays in the lab. If you treat it like a production system, it creates real impact. At Icanio, we’re focused on bridging that gap—turning promising AI ideas into scalable, reliable systems that actually serve users at scale. 💡 The future of AI isn’t just about what models can do. It’s about what systems can sustain. Read the full blog here: https://icanio.com #AI #LLM #Engineering #Scalability #MLOps #SystemDesign #TechLeadership #ArtificialIntelligence #Startups #Innovation

Most teams still think AI maturity = better prompting. That’s Layer 1 thinking. And it’s exactly why most “AI initiatives” never make it past demos. The teams actually shipping durable AI systems in 2026 are climbing 4 engineering layers: 1) Prompt Engineering Useful for fast wins. But nothing compounds. 2) Context Engineering This is where your company becomes legible to machines. Memory, RAG, tool access, structured retrieval. 3) Harness Engineering The model is not the system. The orchestration around it is. Validation, retries, observability, routing, guardrails. 4) Intent Engineering The hardest layer. Encoding what the system should optimize for when instructions run out: trust trade-offs business outcomes long-term user value The biggest shift I’m seeing: The winning AI companies are no longer model-first. They’re becoming systems-first organizations. Because the ceiling of your AI product is rarely the model. It’s: how knowledge flows how failure is contained how feedback compounds how purpose is encoded That’s the real moat. The future won’t belong to teams with the best prompts. It’ll belong to teams with the best AI operating systems. Carousel breaks down all 4 layers ↓ #AI #AIAgents #ContextEngineering #AgenticAI #SystemDesign #Founders #Startups #ProductEngineering #MachineLearning #BuildInPublic

Most AI products still behave like clever assistants. HIL-AIW is built around a different idea: governed AI as a workforce. Today I’m sharing our public architecture and delivery overview for HIL-AIW; a platform model for turning AI roles into a structured, auditable, human-in-the-loop operating system for real work. The core thesis is simple: -AI should work inside governed boundaries -Tasks should move through formal packets, not ad hoc prompt chaos -Durable outputs should be versioned, reviewable, and traceable -Initiative should be proposal-first, not reckless autonomy That means moving beyond “ask a chatbot” and toward: -role-based execution -deterministic workflows -artifact-centric memory -governance and review gates -atlas-bound context and temporal controls In other words: not more AI theater - actual operating discipline for enterprise AI. This overview is intentionally public-facing. It explains where HIL-AIW is headed, how the architecture is structured, and how we think about delivery, safety, and commercialization without exposing internal implementation details that belong in private technical review. I believe the next wave of AI winners will not just be the teams with the smartest models. They’ll be the teams that can make AI: governable, deployable, explainable, and economically usable inside real organizations. That is the lane HIL-AIW is built for. If you’re an: -investor looking at governed AI infrastructure -enterprise operator thinking beyond copilots -partner interested in pilots, architecture, or commercialization I’d love to connect. #HILAIW #ArtificialIntelligence #AIInfrastructure #EnterpriseAI #AIArchitecture #AgenticAI #AIGovernance #HumanInTheLoop #MultiAgentSystems #EnterpriseSoftware #Startup #VentureCapital

Website Link: www.systemdrd.com 🚀 Most AI systems don’t break at the model level — they break at the context layer. We’re entering the era of Context Engineering. I’ve been building a production-grade Context Manager that brings enterprise-level discipline to how AI systems handle context windows — something most demos completely ignore. Here’s what it includes: ⚙️ Intelligent Token Counter → Real-time prompt token tracking using tiktoken 🧠 Multi-Strategy Summarizer → Extractive, abstractive, and hybrid compression techniques 📊 Context Window Optimizer → Automatic pruning, filtering, and prioritization 🖥️ React Dashboard → Live monitoring of efficiency metrics and compression ratios 💡 Key insight: As LLMs scale, context becomes the new bottleneck — not compute, not models. Whoever masters context will build the most efficient, cost-effective, and scalable AI systems. Curious — how are you managing context in your AI stack? #AI #ContextEngineering #LLM #SystemDesign #AIArchitecture #GenerativeAI #SoftwareEngineering #MachineLearning #TechLeadership #AIProducts #Startups #Innovation

CIOs and CTOs should be asking a different AI question now: Which controls were only there because the model was weak? That is why the Mythos leak matters. Not because every leaked detail will prove true. But because it is another signal that model capability is advancing faster than the operating models wrapped around it. A lot of today’s AI workflows were built as compensation: long prompts, hardcoded workflows, brittle retrieval chains, manual review steps, and people acting as glue between systems. Some of that is still necessary. Some of it is already turning into drag. I see this in my own workflows. After each new OpenAI release, I ask Codex to review my AGENTS file and strip out instructions that have become weaker than the model. The file keeps getting shorter. The agent keeps working longer sessions on its own (yes, I find Codex better suited for my work than Claude). That is the shift. As models improve, the bottleneck moves up the stack. The constraint is no longer just model quality. It is architecture, governance, and operating design. If your AI workflow still depends on procedural prompt scripts, too many handoffs, and humans checking work that should be machine-verifiable, you do not have a scaled operating model. You have an expensive pilot. The operating model needs to change: Give the model a clear objective, not a 40-step script. Give it access to clean, well-governed data, not a maze of orchestration. Move human oversight to exception handling, risk decisions, and final accountability. Build a hard eval gate so quality, security, and compliance are tested systematically, not inspected manually after the fact. Less effort spent managing weak-model behavior. More effort spent designing trustworthy, scalable, outcome-driven systems. That is when AI stops looking like a demo and starts looking like enterprise capability. #ai #aistrategy #innovation #future #startups #founders #cto #cio

AI Is Becoming the Core Engine of Modern Software AI is no longer just about models — it is about building intelligent systems. At a technical level, AI systems are built on: • Data pipelines for continuous input and processing • Embedding models for semantic understanding • Inference APIs for real-time predictions • Feedback loops for continuous improvement Key capabilities AI enables: • Intelligent automation • Predictive decision making • Personalized user experiences • Scalable data-driven systems AI is shifting software from static logic to adaptive intelligence. AliAzad Networks builds production-ready AI systems with scalable architecture and real-world deployment. 📧 connect@aliazadnetworks.com 🌐 aliazadnetworks.com #ArtificialIntelligence #AI #MachineLearning #Tech #Startup

If you're building AI systems and stuck on something, I can probably help. Here are 5 things I can turn around in 15 minutes or less. 1. Review your RAG architecture and tell you where it's likely to break. Most retrieval pipelines have the same 3-4 failure points. Chunking strategy, retrieval precision, re-ranking logic, context window misuse. Send me your setup and I'll tell you which one is quietly killing your accuracy. 2. Diagnose why your LLM responses are slow. Latency problems in LLM systems are almost always one of four things, model size, retrieval bottleneck, prompt bloat, or infrastructure misconfiguration. I can usually spot which one from a brief description. 3. Sanity-check your evaluation framework. If you're measuring your AI system's quality with vibes and spot-checks, you're flying blind. I'll tell you what metrics actually matter for your use case and what you're probably missing. 4. Help you decide whether you actually need an LLM for your problem. Genuinely, sometimes you don't. A well-tuned classifier or a structured query pipeline will outperform a bloated LLM setup at a fraction of the cost. I'll give you an honest read. 5. Look at your prompt and tell you why it's underperforming. Bad prompts are the silent killers of otherwise good AI systems. Structure, context injection, instruction clarity, small changes here have outsized impact. Send it over. No pitch. No upsell. I've spent 10 years building these systems. Helping someone think through a problem clearly costs me 15 minutes and might save them weeks. DM me or drop your question in the comments. #ArtificialIntelligence #MachineLearning #Technology #Startups #Innovation

Businesses don't care about your models. They don't care about your tech stack. They don't care which AI buzzword you used. They care about one thing: - Did you solve the problem reliably? Most AI systems don't fail because the model is bad. They fail because the system around the model is weak. Getting to 80–90% accuracy is easy. Getting to production-grade reliability is where the real work begins. That last 10%? It's not an AI problem. It's a data + system design problem. In the real world: - Data is messy - Formats are inconsistent - Edge cases are the system You don't fix this with a better model. You fix it by: - Structuring data upfront - Designing fallback logic - Reducing unnecessary AI calls - Making systems predictable In most systems I've built, the biggest wins didn't come from "more AI". They came from: - Using AI only where needed - Replacing it with logic where possible - Optimizing for cost, latency, and reliability Sometimes: A simple rule > complex model A better pipeline > better architecture Less AI > more value At scale, AI is not about intelligence. It's about reliability per request. Because users don't care how smart your system is. They care that: - it works every time - it's fast - they can trust it The companies that win won't have the best models. They'll build the best systems around the models. #AI #SystemDesign #Engineering #Startups #ProductThinking #MachineLearning #BuildInPublic #TechLeadership