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Category: agentic-ai

  • The Data Team’s Survival Guide for the Next Era of Data

    The Data Team’s Survival Guide for the Next Era of Data 6 pillars to declutter your stack, escape the service trap, and build the missing foundations for the new primary data consumer: the AI agent. The post The Data Team’s Survival Guide for the Next Era of Data appeared first on Towards Data Science. Mahdi…

  • How to Create Production-Ready Code with Claude Code

    How to Create Production-Ready Code with Claude Code Learn how to write robust code with coding agents. The post How to Create Production-Ready Code with Claude Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Escaping the Prototype Mirage: Why Enterprise AI Stalls

    Escaping the Prototype Mirage: Why Enterprise AI Stalls Too many prototypes, too few products The post Escaping the Prototype Mirage: Why Enterprise AI Stalls appeared first on Towards Data Science. Reya Vir Go to original source

  • Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop

    Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop A practical guide to choosing between single-pass pipelines and adaptive retrieval loops based on your use case’s complexity, cost, and reliability requirements The post Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop appeared first on Towards Data Science. Mostafa…

  • Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale

    Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale Reducing LLM costs by 30% with validation-aware, multi-tier caching The post Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale appeared first on Towards Data Science. Partha Sarkar Go to original source

  • Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel

    Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development. The post Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel appeared first on Towards Data Science. Ruben Broekx Go to original source

  • AI Bots Formed a Cartel. No One Told Them To.

    AI Bots Formed a Cartel. No One Told Them To. Inside the research that shows algorithmic price-fixing isn’t a bug in the code. It’s a feature of the math. The post AI Bots Formed a Cartel. No One Told Them To. appeared first on Towards Data Science. Kaushik Rajan Go to original source

  • The Reality of Vibe Coding: AI Agents and the Security Debt Crisis

    The Reality of Vibe Coding: AI Agents and the Security Debt Crisis Why optimizing for speed over safety is leaving applications vulnerable, and how to fix it. The post The Reality of Vibe Coding: AI Agents and the Security Debt Crisis appeared first on Towards Data Science. Reya Vir Go to original source

  • An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI

    An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI The guide to automated improvement of scientific and industrial repositories using open-source AI agents The post An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI appeared first on Towards Data Science. Nikolay Nikitin Go to original source

  • The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents

    The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents AI can write the code, but you have to steer the ship. Master the knowledge to keep you relevant in the age of AI. The post The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding…

  • Can AI Solve Failures in Your Supply Chain?

    Can AI Solve Failures in Your Supply Chain? When your warehouse and transportation teams blame each other for late deliveries, who’s right? We can ask an agent connected to the data settle the debate. The post Can AI Solve Failures in Your Supply Chain? appeared first on Towards Data Science. Samir Saci Go to original…

  • Agentic AI for Modern Deep Learning Experimentation

    Agentic AI for Modern Deep Learning Experimentation Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. The post Agentic AI for Modern Deep Learning Experimentation appeared first on Towards Data Science. Sam Black Go to original source

  • Use OpenClaw to Make a Personal AI Assistant

    Use OpenClaw to Make a Personal AI Assistant Learn how to set up OpenClaw as a personalized AI agent The post Use OpenClaw to Make a Personal AI Assistant appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Building a LangGraph Agent from Scratch

    Building a LangGraph Agent from Scratch Everything you need to know to get started The post Building a LangGraph Agent from Scratch appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Building an AI Agent to Detect and Handle Anomalies in Time-Series Data

    Building an AI Agent to Detect and Handle Anomalies in Time-Series Data Combining statistical detection with agentic decision-making The post Building an AI Agent to Detect and Handle Anomalies in Time-Series Data appeared first on Towards Data Science. MADHURA RAUT Go to original source

  • The Death of the “Everything Prompt”: Google’s Move Toward Structured AI

    The Death of the “Everything Prompt”: Google’s Move Toward Structured AI How the new Interactions API enables deep-reasoning, stateful, agentic workflows. The post The Death of the “Everything Prompt”: Google’s Move Toward Structured AI appeared first on Towards Data Science. Thomas Reid Go to original source

  • Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

    Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes How much of your AI agent’s output is real data versus confident guesswork? The post Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes appeared first on Towards Data Science. James Barney Go to original source

  • How to Work Effectively with Frontend and Backend Code

    How to Work Effectively with Frontend and Backend Code Learn how to be an effective full-stack engineer with Claude Code The post How to Work Effectively with Frontend and Backend Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Plan–Code–Execute: Designing Agents That Create Their Own Tools

    Plan–Code–Execute: Designing Agents That Create Their Own Tools The case against pre-built tools in Agentic Architectures The post Plan–Code–Execute: Designing Agents That Create Their Own Tools appeared first on Towards Data Science. Partha Sarkar Go to original source

  • How to Apply Agentic Coding to Solve Problems

    How to Apply Agentic Coding to Solve Problems Learn how to efficiently solve problems with coding agents The post How to Apply Agentic Coding to Solve Problems appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”

    Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types. The post Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” appeared first…

  • How to Perform Large Code Refactors in Cursor

    How to Perform Large Code Refactors in Cursor Learn how to perform code refactoring with LLMs The post How to Perform Large Code Refactors in Cursor appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Maximum-Effiency Coding Setup

    Maximum-Effiency Coding Setup Learn how to be a more efficient programmer The post Maximum-Effiency Coding Setup appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Run Coding Agents in Parallel

    How to Run Coding Agents in Parallel Get the most out of Claude Code The post How to Run Coding Agents in Parallel appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Maximize Claude Code Effectiveness

    How to Maximize Claude Code Effectiveness Learn how to get the most out of agentic coding The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example

    Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI’s GPT 5.2 The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science. Vincent Koc Go to…

  • How to Leverage Slash Commands to Code Effectively

    How to Leverage Slash Commands to Code Effectively Learn how I utilize slash commands to be a more efficient engineer The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Beyond Prompting: The Power of Context Engineering

    Beyond Prompting: The Power of Context Engineering Using ACE to create self-improving LLM workflows and structured playbooks The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • How to Optimize Your AI Coding Agent Context

    How to Optimize Your AI Coding Agent Context Make your coding agents more efficient The post How to Optimize Your AI Coding Agent Context appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Keep MCPs Useful in Agentic Pipelines

    How to Keep MCPs Useful in Agentic Pipelines Check the tools your LLM uses before replacing it with just a more powerful model The post How to Keep MCPs Useful in Agentic Pipelines appeared first on Towards Data Science. Roman S Go to original source

  • Production-Ready LLMs Made Simple with the NeMo Agent Toolkit

    Production-Ready LLMs Made Simple with the NeMo Agent Toolkit From simple chat to multi-agent reasoning and real-time REST APIs The post Production-Ready LLMs Made Simple with the NeMo Agent Toolkit appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Agents Under the Curve (AUC)

    Agents Under the Curve (AUC) Towards understanding if your agentic solution is actually better The post Agents Under the Curve (AUC) appeared first on Towards Data Science. Lambert Leong Go to original source

  • How IntelliNode Automates Complex Workflows with Vibe Agents

    How IntelliNode Automates Complex Workflows with Vibe Agents Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise…

  • How Agents Plan Tasks with To-Do Lists

    How Agents Plan Tasks with To-Do Lists Understanding the process behind agentic planning and task management in LangChain The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Tools for Your LLM: a Deep Dive into MCP

    Tools for Your LLM: a Deep Dive into MCP MCP is a key enabler into turning your LLM into an agent by providing it with tools to retrieve real-time information or perform actions. In this deep dive we cover how MCP works, when to use it, and what to watch out for. The post Tools…

  • Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC)

    Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC) Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and optimization workflows. The post Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC) appeared first on Towards Data Science. Gal Arav Go to original source

  • Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach

    Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach LLM-as-a-Judge, regression testing, and end-to-end traceability of multi-agent LLM systems The post Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach appeared first on Towards Data Science. Partha Sarkar Go to original source

  • Lessons Learned from Upgrading to LangChain 1.0 in Production

    Lessons Learned from Upgrading to LangChain 1.0 in Production What worked, what broke, and why I did it The post Lessons Learned from Upgrading to LangChain 1.0 in Production appeared first on Towards Data Science. Clara Chong Go to original source

  • How to Increase Coding Iteration Speed

    How to Increase Coding Iteration Speed Learn how to become a more efficient programmer with local testing The post How to Increase Coding Iteration Speed appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Decentralized Computation: The Hidden Principle Behind Deep Learning

    Decentralized Computation: The Hidden Principle Behind Deep Learning Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI…

  • How Agent Handoffs Work in Multi-Agent Systems

    How Agent Handoffs Work in Multi-Agent Systems Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph The post How Agent Handoffs Work in Multi-Agent Systems appeared first on Towards Data Science. Kenneth Leung Go to original source

  • How to Maximize Agentic Memory for Continual Learning

    How to Maximize Agentic Memory for Continual Learning Learn how to become an effective engineer with continual learning LLMs The post How to Maximize Agentic Memory for Continual Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot

    Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access. The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first…

  • How We Are Testing Our Agents in Dev

    How We Are Testing Our Agents in Dev Testing that your AI agent is performing as expected is not easy. Here are a few strategies we learned the hard way. The post How We Are Testing Our Agents in Dev appeared first on Towards Data Science. Michael Segner Go to original source

  • Multi-Agent Arena: Insights from London Great Agent Hack 2025

    Multi-Agent Arena: Insights from London Great Agent Hack 2025 What mattered: robust agents, glass-box reasoning, and red-team resilience The post Multi-Agent Arena: Insights from London Great Agent Hack 2025 appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • How to Scale Your LLM Usage

    How to Scale Your LLM Usage Learn how to increase LLM usage to achieve increased productivity The post How to Scale Your LLM Usage appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It

    Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It A real-world analysis of why CrewAI’s hierarchical orchestration misfires—and a practical fix you can implement today. The post Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It appeared first on Towards Data Science. Partha Sarkar Go to original source

  • How to Perform Agentic Information Retrieval

    How to Perform Agentic Information Retrieval Learn how to utilize AI agents to find information in your document corpus The post How to Perform Agentic Information Retrieval appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2

    Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2 In part 1, we showed how we could leverage HTMX to add interactivity to our HTML elements. In other words, Javascript without Javascript. To illustrate that, we began building a simple chat that would return a simulated LLM response. In this article,…

  • How to Automate Workflows with AI

    How to Automate Workflows with AI Learn how to take a manual process and optimize it using AI The post How to Automate Workflows with AI appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Build Agents with GPT-5

    How to Build Agents with GPT-5 Learn how to use GPT-5 as a powerful AI Agent on your data. The post How to Build Agents with GPT-5 appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Build Your Own Agentic AI System Using CrewAI

    How to Build Your Own Agentic AI System Using CrewAI This article demonstrates how to develop your own Agentic AI system using CrewAI framework. By orchestrating specialized agents with distinct roles and tools, we implement a multi-agent team that is capable of generating optimized content for different social media platforms. The post How to Build…

  • TDS Newsletter: The Theory and Practice of Using AI Effectively

    TDS Newsletter: The Theory and Practice of Using AI Effectively When we encounter a new technology — say, LLM applications — some of us tend to jump right in, sleeves rolled up, impatient to start tinkering. Others prefer a more cautious approach: reading a few relevant research papers, or browsing through a bunch of blog posts, with…

  • Build LLM Agents Faster with Datapizza AI

    Build LLM Agents Faster with Datapizza AI Intro Organizations are increasingly investing in AI as these new tools are adopted in everyday operations more and more. This continuous wave of innovation is fueling the demand for more efficient and reliable frameworks. Following this trend, Datapizza (the startup behind Italy’s tech community) just released an open-source…

  • Agentic AI from First Principles: Reflection

    Agentic AI from First Principles: Reflection From theory to code: building feedback loops that improve LLM accuracy The post Agentic AI from First Principles: Reflection appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Deploy an OpenAI Agent Builder Chatbot to a Website

    Deploy an OpenAI Agent Builder Chatbot to a Website Using OpenAI’s Agent Builder ChatKit The post Deploy an OpenAI Agent Builder Chatbot to a Website appeared first on Towards Data Science. Thomas Reid Go to original source

  • Implementing DRIFT Search with Neo4j and LlamaIndex

    Implementing DRIFT Search with Neo4j and LlamaIndex Combining global and local search to get the most accurate response The post Implementing DRIFT Search with Neo4j and LlamaIndex appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • Agentic AI in Finance: Opportunities and Challenges for Indonesia

    Agentic AI in Finance: Opportunities and Challenges for Indonesia The rise of AI has touched nearly every industry — including finance. In fact, the financial sector has long been an adopter of what we now call “traditional machine learning,” using it for predictive modeling, credit scoring, and risk analytics. But with the current hype around…

  • Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI

    Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI Context engineering, semantic layers, and the evolution of retrieval for agentic AI The post Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI appeared first on Towards Data Science. Steve Hedden Go to original source

  • How to Build An AI Agent with Function Calling and GPT-5

    How to Build An AI Agent with Function Calling and GPT-5 How an AI agent works: a step-by-step guide The post How to Build An AI Agent with Function Calling and GPT-5 appeared first on Towards Data Science. Ayoola Olafenwa Go to original source

  • How to Build Guardrails for Effective Agents

    How to Build Guardrails for Effective Agents Learn how to set up effective guardrails to enforce desired behaviour from your agents The post How to Build Guardrails for Effective Agents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Build Tools for AI Agents

    How to Build Tools for AI Agents Learn how to design and build effective tools to be used by AI Agents The post How to Build Tools for AI Agents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • 10 Data + AI Observations for Fall 2025

    10 Data + AI Observations for Fall 2025 What’s happening—and what’s next— for data and AI at the close of 2025. The post 10 Data + AI Observations for Fall 2025 appeared first on Towards Data Science. Barr Moses Go to original source

  • How to Build Effective Agentic Systems with LangGraph

    How to Build Effective Agentic Systems with LangGraph Create AI workflows with agentic frameworks The post How to Build Effective Agentic Systems with LangGraph appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • MCP in Practice

    MCP in Practice Mapping power, concentration, and usage in the emerging AI developer ecosystem The post MCP in Practice appeared first on Towards Data Science. Sruly Rosenblat Go to original source

  • Using Vision Language Models to Process Millions of Documents

    Using Vision Language Models to Process Millions of Documents Learn how to effectively apply vision language models to problem solving The post Using Vision Language Models to Process Millions of Documents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Connect an MCP Server for an AI-Powered, Supply-Chain Network Optimization Agent

    How to Connect an MCP Server for an AI-Powered, Supply-Chain Network Optimization Agent From prompt to strategic decision-making: MCP-powered agents for cost-efficient, reliable and sustainable supply chain network design. The post How to Connect an MCP Server for an AI-Powered, Supply-Chain Network Optimization Agent appeared first on Towards Data Science. Samir Saci Go to original…

  • TDS Newsletter: How to Make Smarter Business Decisions with AI

    TDS Newsletter: How to Make Smarter Business Decisions with AI Research agents, budget planners, and more The post TDS Newsletter: How to Make Smarter Business Decisions with AI appeared first on Towards Data Science. TDS Editors Go to original source

  • You Only Need 3 Things to Turn AI Experiments into AI Advantage

    You Only Need 3 Things to Turn AI Experiments into AI Advantage Trapped in a purgatory of POCs enterprises need to focus and build just 3 pillars to realize value from AI The post You Only Need 3 Things to Turn AI Experiments into AI Advantage appeared first on Towards Data Science. Shreshth Sharma Go…

  • The Rise of Semantic Entity Resolution

    The Rise of Semantic Entity Resolution Semantic entity resolution uses language models to bring an increased level of automation to schema alignment, blocking (grouping records into smaller, efficient blocks for all-pairs comparison at quadratic, n² complexity), matching and even merging duplicate nodes and edges. In the past, entity resolution systems relied on statistical tricks such…

  • Building Research Agents for Tech Insights

    Building Research Agents for Tech Insights Using a controlled workflow, unique data & prompt chaining The post Building Research Agents for Tech Insights appeared first on Towards Data Science. Ida Silfverskiöld Go to original source

  • Generalists Can Also Dig Deep

    Generalists Can Also Dig Deep Ida Silfverskiöld on AI agents, RAG, evals, and what design choice ended up mattering more than expected The post Generalists Can Also Dig Deep appeared first on Towards Data Science. TDS Editors Go to original source

  • LangChain for EDA: Build a CSV Sanity-Check Agent in Python

    LangChain for EDA: Build a CSV Sanity-Check Agent in Python A practical LangChain tutorial for data scientists to inspect CSVs The post LangChain for EDA: Build a CSV Sanity-Check Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source

  • How to Build Effective AI Agents to Process Millions of Requests

    How to Build Effective AI Agents to Process Millions of Requests Learn how to build production ready systems using AI agents The post How to Build Effective AI Agents to Process Millions of Requests appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Agentic AI and the Future of Python Project Management Tooling

    Agentic AI and the Future of Python Project Management Tooling Introducing a pyramid framework of evolution, accelerating and decelerating factors, and strategic recommendations for incumbents and new entrants The post Agentic AI and the Future of Python Project Management Tooling appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs

    Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs How timeouts, truncation, and result sanitization keep Cypher outputs LLM-ready The post Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails

    Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails A practical exploration of how guardrails safeguard multi-agent systems in Python using OpenAI Agents SDK, Streamlit, and Pydantic The post Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source

  • Tool Masking: The Layer MCP Forgot

    Tool Masking: The Layer MCP Forgot Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and errors, boost speed and reliability. Start prompt engineering your tools The post Tool Masking: The Layer MCP Forgot appeared first on Towards Data Science. Frank Wittkampf Go to original source

  • Should We Use LLMs As If They Were Swiss Knives?

    Should We Use LLMs As If They Were Swiss Knives? A logic game performance comparison between popular LLMs and a custom-made algorithm The post Should We Use LLMs As If They Were Swiss Knives? appeared first on Towards Data Science. Nicolas Garcia Aramouni Go to original source

  • Using LangGraph and MCP Servers to Create My Own Voice Assistant

    Using LangGraph and MCP Servers to Create My Own Voice Assistant Built over 14 days, all locally run, no API keys, cloud services, or subscription fees. The post Using LangGraph and MCP Servers to Create My Own Voice Assistant appeared first on Towards Data Science. Benjamin Lee Go to original source

  • Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals

    Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals Explore how Agentic AI is reshaping the tech careers, from data to decision-making, and how professionals can prepare for the future of work The post Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals appeared first…

  • AI Agents for Supply Chain Optimisation: Production Planning

    AI Agents for Supply Chain Optimisation: Production Planning How to integrate an optimisation algorithm in a FastAPI microservice and connect it with an AI workflow to automate production planning. The post AI Agents for Supply Chain Optimisation: Production Planning appeared first on Towards Data Science. Samir Saci Go to original source

  • Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance

    Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance Automating model tuning in Python with Gemini, LangGraph, and Streamlit for regression and classification improvements The post Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance appeared first on Towards Data Science. Gustavo Santos Go to…

  • LangGraph 101: Let’s Build A Deep Research Agent

    LangGraph 101: Let’s Build A Deep Research Agent Learn LangGraph fundamentals from Google’s open-source full-stack implementation The post LangGraph 101: Let’s Build A Deep Research Agent appeared first on Towards Data Science. Shuai Guo Go to original source

  • LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions

    LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions Stop guessing your statistical test. Let this AI do it for you. The post LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions appeared first on Towards Data Science. Gustavo Santos Go to original source

  • Finding Golden Examples: A Smarter Approach to In-Context Learning

    Finding Golden Examples: A Smarter Approach to In-Context Learning From random example selection to systematic AuPair generation  — how to make your LLM prompts actually work The post Finding Golden Examples: A Smarter Approach to In-Context Learning appeared first on Towards Data Science. Sudheer Singh Go to original source

  • Agentic AI: On Evaluations

    Agentic AI: On Evaluations Metrics to track for RAG and agents, plus the frameworks that help The post Agentic AI: On Evaluations appeared first on Towards Data Science. Ida Silfverskiöld Go to original source

  • The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons

    The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons Unless you’re someone who lives and breathes cybersecurity, chances are you didn’t think much about authentication, network exposure, or what happens if someone else finds your server. This guide isn’t here to kill the excitement—it’s here to help you use MCP without opening the…

  • Hands-On with Agents SDK: Multi-Agent Collaboration

    Hands-On with Agents SDK: Multi-Agent Collaboration Explore the handoff and agents-as-tools patterns, their use cases, and how to customize them using OpenAI Agents SDK and Streamlit. The post Hands-On with Agents SDK: Multi-Agent Collaboration appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source

  • The Stanford Framework That Turns AI into Your PM Superpower

    The Stanford Framework That Turns AI into Your PM Superpower A human-centric guide to AI automation for product managers. The post The Stanford Framework That Turns AI into Your PM Superpower appeared first on Towards Data Science. Rahul Vir Go to original source

  • Talk to my Agent 

    Talk to my Agent  The exciting new world of designing conversation driven APIs for LLMs. The post Talk to my Agent  appeared first on Towards Data Science. Roni Dover Go to original source

  • Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide

    Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide Learn how to create AI Agents using the OpenAI Agents SDK to automate Jira ticket creation from a meeting transcript. The post Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide appeared first on Towards Data Science. Juan…

  • Hands‑On with Agents SDK: Your First API‑Calling Agent

    Hands‑On with Agents SDK: Your First API‑Calling Agent A practical, beginner‑friendly guide to building an AI weather assistant with Python, OpenAI Agents SDK, API tools, and Streamlit. The post Hands‑On with Agents SDK: Your First API‑Calling Agent appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source

  • Midyear 2025 AI Reflection

    Midyear 2025 AI Reflection Impressions on agentic AI progress and the AI-2027 Jobocalypse scenario The post Midyear 2025 AI Reflection appeared first on Towards Data Science. Marina Tosic Go to original source

  • The Power of Building from Scratch

    The Power of Building from Scratch Mauro Di Pietro discusses building AI agents with open-source tools, bridging theory and practice, and why he’s still nostalgic for scikit-learn. The post The Power of Building from Scratch appeared first on Towards Data Science. TDS Editors Go to original source

  • Building a Сustom MCP Chatbot

    Building a Сustom MCP Chatbot Understanding all the details of the model context protocol The post Building a Сustom MCP Chatbot appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • AI Agents Are Shaping the Future of Work Task by Task, Not Job by Job

    AI Agents Are Shaping the Future of Work Task by Task, Not Job by Job What two groundbreaking studies reveal about the future of human-AI collaboration, and the enterprise playbook for thriving in the AI agent era The post AI Agents Are Shaping the Future of Work Task by Task, Not Job by Job appeared…

  • Microsoft’s Revolutionary Diagnostic Medical AI, Explained

    Microsoft’s Revolutionary Diagnostic Medical AI, Explained Microsoft’s latest paper discusses a path to medical superintelligence. How close are we, really? The post Microsoft’s Revolutionary Diagnostic Medical AI, Explained appeared first on Towards Data Science. Ryan D’Cunha Go to original source

  • GraphRAG in Action: A Simple Agent for Know-Your-Customer Investigations

    GraphRAG in Action: A Simple Agent for Know-Your-Customer Investigations This blog post provides a hands-on guide for AI engineers and developers on how to build an initial KYC agent prototype with the OpenAI Agents SDK. We’ll explore how to equip our agent with a suite of tools (including MCP Server tools) to uncover and investigate potential…

  • Agentic AI: Implementing Long-Term Memory

    Agentic AI: Implementing Long-Term Memory The problem and current solutions The post Agentic AI: Implementing Long-Term Memory appeared first on Towards Data Science. Ida Silfverskiöld Go to original source