Author's): Towards the AI editorial office
Originally published in Towards Artificial Intelligence.
Good morning AI lovers,
This week we explore how AI systems are becoming more structured, contextual and multimodal. We explore how vision language models like GPT-4o and Qwen 2.5 VL are redefining what it means for AI to “see” and “understand” in use cases spanning manufacturing, healthcare, and device deployment.
Then we will move on to the plans for intelligent systems. You'll find a practical guide to building agent-based workflows with LangGraph, from routing and reflection to multi-agent collaboration; a complete guide to building a text-based chatbot on SQL that combines natural language with databases, and a detailed discussion of how efficient memory architectures – from hierarchical systems to selective forgetting – ensure agent stability and scalability.
Let's get on with it.
—Louis-François Bouchard, co-founder and head of Community towards AI
Get to Know Artificial Intelligence Community Section!
Featured Community Post from Discord
Eschnow has published a new open source project, Patchsmith, which focuses on AI-powered static code analysis. It includes a classic code analyzer (CodeQL) with an agent layer (Claude SDK) to write custom queries based on code and hints, segregate SARIF results, group issues, extract the most important ones, investigate issues for risks, false positives, etc. and prepare pull requests with fixes. Check it out on GitHub and support another community member. If you have any comments, share it in the thread!
AI Poll of the Week!
The peace is leaning towards China, where a clear minority still supports the US. People are probably reacting to the pace of recent releases and coordination in China, compared to the U.S. advantage in frontier labs, chips, and the research-to-product pipeline. Name two specific signals you will see in 2025 that could change your view, e.g., national computer supply, evaluation wins tied to delivered products, corporate adoption or policy changes, and the threshold that will be considered decisive. Let's talk in the thread!
Collaboration opportunities
The Learn AI Together Discord community is full of collaborative opportunities. If you're excited to dive into applied AI, are looking for a learning partner, or even want to find a partner for your passion project, join the collaboration channel! Also follow this section – we share cool opportunities every week!
1. Mr. Jack45 is building an AI-powered application that brings innovation to the electric vehicle ecosystem and is looking for people who want to join forces and create something that will make an impact. If you are interested in artificial intelligence, app development, design or business development, connect with him in the thread!
2. Grit_george is looking for a fast-paced staff-level engineer, someone who has a deep understanding of how to structure and coordinate the behavior of a large model for complex, dynamic tasks. If this sounds important, contact them in the thread!
3. Just_chilling7 is creating a portability layer to make it easier to migrate tooltips, embeddings, and tuning between models such as OpenAI, Claude, Gemini, and Llama, and is looking for a technical ML expert. If this sounds like you, contact them in the thread!
Meme of the week!
Meme shared by bin4ry_d3struct0r
Section curated by TAI
Article of the week
The future is already here: multimodal and visionary models transforming artificial intelligence Abhinaya Pinreddy
Artificial intelligence goes beyond specialized text and image models and is moving towards integrated systems that process both simultaneously. This article examines multimodal artificial intelligence, with a focus on vision models (VLM). It covers architecture, training processes, and applications in industries such as healthcare and manufacturing. It also provides an overview of current models, including the open-source GPT-4o and Qwen 2.5 VL. It addresses implementation challenges such as computational costs and hallucinations, while also exploring future developments, including agent capabilities and on-device deployment, to provide a balanced view of the current state of the technology.
Our must-read articles
1. Mastering Design Patterns with LangGraph: The Complete Guide to Building Intelligent AI Systems by Mahendra Medapati
Structural engineering is required to move AI agents from demos to production. This guide provides a practical framework for building reliable systems using LangGraph. It details seven agent design patterns, including routing, reflection, and multi-agent collaboration, providing code examples and diagrams for each. The article shows how to create adaptive workflows for complex tasks, based on real-world applications from companies such as Uber and Replit.
2. Building an AI-enabled chatbot that can convert text to SQL: your data's new best friend Abhinaya Pinreddy
This article describes the development of an AI-enabled Text-to-SQL chatbot designed to make database queries accessible to non-technical users. Explains how search-assisted generation (RAG) is used to provide AI with specific database schema context, enabling it to generate accurate SQL queries from natural language questions while avoiding common errors. It presents the complete architecture, from the SQLite database and vector store to the LangChain orchestrator and Streamlit UI. The process includes schema indexing, query generation, security verification to prevent unauthorized operations, and execution to return results via a simple chat interface.
3. How to build effective agent systems using LangGraph By Eivind Kjosbakken
This article discusses the construction of agentic AI systems using LangGraph, organizing them as graphs of nodes and edges. It demonstrates this approach by creating a document management workflow in which the router first classifies user intent – such as adding, deleting, or searching – before executing the actual function. The article highlights how such frameworks simplify complex processes such as state management and tool usage. It also provides a balanced look at LangGraph, taking into account its advantages, such as open source and ease of configuration, as well as its disadvantages, including the need for boilerplate code.
4. How to design efficient memory architectures for agentic AI systems Author: Suchitra Malimbada
To ensure that AI agents can maintain long conversations and maintain factual accuracy, a strategic approach to memory design is necessary. This element goes beyond basic vector storage, which can lead to contextual errors and high costs at scale. It introduces several advanced architectures, including hierarchical memory systems such as MemGPT, to efficiently manage extended dialogues. For applications requiring verifiable reasoning, the use of knowledge graphs is described in detail. It also includes selective forgetting mechanisms to remove obsolete information, optimize performance, and reduce storage costs for long-running agents.
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