Aimen Bouzid’s Post

Most event tech stacks are optimizing the wrong thing—and AI is making it worse. Not because the tools are bad, but because the decisions are happening too early. This article lays out the shift from automation to decision architecture. ▸ https://lttr.ai/Ams4c #EventTechnology #SystemsThinking #ArtificialIntelligence #AttendeeJourney #Businesstech

Most event tech stacks are optimizing the wrong thing—and AI is making it worse. Not because the tools are bad, but because the decisions are happening too early. This article lays out the shift from automation to decision architecture. ▸ https://lttr.ai/Ams2H #EventTechnology #SystemsThinking #ArtificialIntelligence #AttendeeJourney #Businesstech

Building AI Ready Enterprise: Lessons from the Human Brain 90% of AI initiatives fail because we force fluid intelligence into rigid, legacy IT structures. To reach an 80% success rate, we must build a Brain-Inspired Enterprise: 🧠 Orchestration Layer: Acts as the "frontal lobe" to manage complex agent tasks. 🔗 Model Context Protocol (MCP): Exposes app data and tools in a way AI can actually understand. ⚡ Integrated Intelligence: Agents act as dynamic synapses, connecting silos on demand. Stop building static pipelines; start building an adaptive nervous system. Is your architecture ready for the agentic future? #AIArchitecture #EnterpriseAI #MCP #SystemDesign #Innovation

The question isn't whether you CAN connect your client directly to an agent, it's whether you SHOULD. 💭 Building #AI agents? Here's what separates quick demos from production-ready systems: Short-term thinking: Direct client-to-agent connections Long-term success: Well-structured architecture around your agents Why architecture matters for AI agents: • Prevents brittle systems that break easily • Makes your codebase easier to understand and maintain • Reduces long-term costs and technical debt • Lets agents focus on reasoning, not infrastructure concerns Think of it this way: 🧠 The agent = the brain (handles intelligent reasoning) 🦴 The architecture = the body (handles everything else) Both need to work together for a production system that scales. 📈 Read the full article to learn more ➡️ https://go.aws/49c2W6m

Building the Blueprint for Production-Grade Agentic AI If you’re wondering how enterprises are planning real autonomous systems based on agents — here’s the blueprint theme emerging across AWS: Core pillars driving successful implementations: 🏗️ Tool orchestration powered by semantic search 🧠 Memory-enhanced agent reasoning 🔐 Identity & governance baked into the architecture 🧰 Unified gateway for tools + APIs ⚡ Async and multi-agent collaboration patterns 📊 Deep observability and auditing ☁️ Model-agnostic runtime and scalable infra This is how AI moves from PoCs to production environments — with structure, visibility and responsibility. If your organization is exploring production-grade Agentic AI, curious to compare notes and share architecture patterns. #AgenticAI #AWSBedrock #CloudArchitecture #FutureOfWork #AIInEnterprise

Choosing the right agentic AI framework is critical if you want to move beyond demos into production. Our latest white paper compares Crew AI, Google ADK, LangGraph, and the OpenAI Agents SDK, showing how each one fits different needs—from rapid multi-agent prototyping to durable, stateful workflows and cloud-native enterprise agentic AI deployments. It looks at production readiness across: ✔️ Tool and MCP server integration ✔️ Memory architecture ✔️ Human-in-the-loop (HITL) support ✔️ Guardrails integration ✔️ Workflow orchestration models ✔️ Agent abstraction and reasoning patterns (Chain-of-Thought, Graph-of-Thoughts, multi-agent collaboration) 👉 Download the white paper for detailed implementation examples, architecture diagrams, and feature-by-feature breakdowns to help you choose the right framework for your stack.

Models can learn what data they need better than humans. That is the real takeaway from this paper, not bigger models or more parameters, but systems that adapt their data appetite based on performance signals we cannot reliably specify upfront. If your AI architecture still treats data selection as a static human decision, you are leaving capability on the table. #AIStrategy #Builders

Announcing #MLRun CE - Community Edition! We’re proud to announce the availability of the out-of-the-box MLRun solution for #AI and #ML orchestration and model lifecycle management. After an easy Helm chart installation, you get a powerful, integrated environment for development built on MLRun for #MLOps orchestration and Nuclio for serverless computing. Read the full blog by Gilad Shapira to discover how it works, ecosystem components and use cases. We also share how one of our users used MLRun CE to: 📊 Run experiment tracking 🔒 Ensure model integrity 🔁 Duplicate experiments 🛠️ Easily debug experiment logs And more Read the full blog: https://hubs.la/Q03YBLP50

Build your own AI based monitoring on Databricks? Or buy Anodot ? We just published a comparison guide (link in first comment). TL;DR Anodot delivers turnkey, low‑noise anomaly detection with cross‑metric correlation, significance scoring, event-aware baselines, and an incident UX at enterprise scale. Databricks‑built solutions can reach high accuracy using your choice of models and Spark‑scale pipelines, but you must assemble feature engineering, training/retraining, correlation logic, alerting, and the incident workflow.

Building your own AI based solution is easier these days on platforms like Databricks. But, how does the effort compare to buying a solution from Anodot? This guide compares the two. https://lnkd.in/dunzvhNZ