As Large Language Models (LLMs) evolve, the challenge shifts from raw capability to structuring them into reliable, scalable systems. Many real-world AI products struggle with robustness, complexity management, and evaluation—especially in enterprise contexts. This talk explores how multi-agent systems can help overcome these obstacles by decomposing large monolithic agents into specialized subagents working together in structured architectures. We’ll cover: - Why enterprises struggle to integrate LLM agents effectively. - How multi-agent architectures (Assembly Line, Call Center, and Manager-Worker) improve scalability, modularity, and reliability. - Practical trade-offs and implementation strategies from real-world applications. (planning to adapt my post https://blog.sshh.io/p/building-multi-agent-systems)

Artificial Intelligence is transforming the way we interact with technology, and Agentic AI—systems that exhibit autonomy, adaptability, and decision-making capabilities—is at the forefront of this revolution. But what does this mean for the Arabic language, one of the richest and most complex languages in the world? As we advance AI-driven agents, ensuring they understand, process, and generate Arabic with the same fluency and nuance as English or other dominant languages is not just a technological challenge but a cultural imperative. In this speech, we will explore how Agentic AI can empower Arabic speakers, enhance accessibility, and preserve the linguistic heritage of over 400 million people while driving innovation across industries. The future of AI is agentic. The future of Arabic in AI depends on how we shape it today. Artificial Intelligence is transforming the way we interact with technology, and Agentic AI—systems that exhibit autonomy, adaptability, and decision-making capabilities—is at the forefront of this revolution. But what does this mean for the Arabic language, one of the richest and most complex languages in the world? As we advance AI-driven agents, ensuring they understand, process, and generate Arabic with the same fluency and nuance as English or other dominant languages is not just a technological challenge but a cultural imperative. In this speech, we will explore how Agentic AI can empower Arabic speakers, enhance accessibility, and preserve the linguistic heritage of over 400 million people while driving innovation across industries. The future of AI is agentic. The future of Arabic in AI depends on how we shape it today.

In this episode, we talk with Kenny Daniel, founder of Hyperparam, to explore why actually looking at your data is the most high-leverage move you can make for building state-of-the-art models. It used to be that the first step of data science was to get familiar with your data. However, as modern LLM datasets have gotten larger, dataset exploration tools have not kept up. Kenny makes the case that user interfaces have been under-appreciated in the Python-centric world of AI, and new tools are needed to enable advances in machine learning. Our conversation also dives into new methods of using LLM models themselves to assist data engineers in actually looking at their data.

Google’s Gemini 1.5, with a 2 million-token context window, was tested in the "Gemini Long Context" Kaggle competition. The author built an AI-powered interactive textbook, leveraging Gemini to retrieve, process, and personalize learning from open-source textbooks. The project showed promise in accuracy and cross-topic learning but faced challenges with execution speed and API inefficiencies. Despite hurdles, long-context AI has the potential to revolutionize education with adaptive, AI-driven tutors.

There seems to be a shift from workflow languages to code - mostly annotation pythons - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs and at MicroByre, we used a external workflow langauge. Both had a batch task executor on K8s but at MicroByre, we had human and robot in the loop workflows.

This survey examines popular workflow orchestration systems, highlighting their strengths, weaknesses, and key features. It provides insights into how these tools handle automation, scalability, and reliability, helping teams choose the right solution for their needs.

In this MLOps Community Podcast episode, Willem Pienaar, CTO of Cleric, breaks down how they built an autonomous AI SRE that helps engineering teams diagnose production issues. We explore how Cleric builds knowledge graphs for system understanding, and uses existing tools/systems during investigations. We also get into some gnarly challenges around memory, tool integration, and evaluation frameworks, and some lessons learned from deploying to engineering teams.

This blog post kicks off a delicious deep-dive into ETL pipelines, but instead of boring technical jargon, it's all about cooking up data! Think of it as a multi-course data feast, and Dataform is the cookbook and kitchen. The goal? To transform raw, messy data into beautifully plated, insightful dishes for your customers. The post breaks down the process, focusing on Dataform, one of the main tools of our ETL meal. Dataform isn't just another tool; it's your digital cookbook, keeping your data transformation recipes organized and error-free. Forget messy spreadsheets – Dataform brings the order and precision of a Michelin-star kitchen to your data. It dives into why Dataform rocks: modular recipes, version control (think "undo" button for data blunders!), built-in taste testing, and clear documentation. The post then gets hands-on, walking you through creating a Dataform project, writing a basic SQLX recipe, and testing it out before committing to a full-blown transformation. Think of it as prepping your mise en place before firing up the stove. Finally, it stresses the importance of sharing your recipe, using version control and Git. Basically, Dataform lets you transform your raw data into gourmet insights, and this post is just the appetizer for the complete ETL feast. Stay tuned for the next courses, where they'll tackle orchestration and data governance! Get ready to cook!