Artificial Intelligence (AI) has rapidly evolved, transforming various industries and applications. From chatbots that enhance customer service to autonomous systems that optimize logistics, AI agents are becoming integral to modern technology. These agents leverage machine learning and natural language processing to understand and respond to user queries effectively. As organizations increasingly adopt AI solutions, understanding their capabilities and limitations is crucial for maximizing their potential. This exploration of AI agents highlights their development, implementation, and the challenges faced in ensuring reliable and efficient performance across diverse applications.

What Happens When Demand Forecasting Becomes Too Big for Traditional Models?

 Towards AI

When forecasting reaches tens of thousands of products, the biggest challenge may no longer be building a better model. It may be building a forecasting capability that can learn, scale and operate e...

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5 MCP Connections That Turn Claude Code Into a Chief of Staff

 Towards AI

Part 1 covered workflows on your files. This one plugs the agent into your calendar, Slack, and email, so it works on live data. Continue reading on Towards AI

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Building a Billion-Vector Search System Without Putting Everything in RAM

 Towards AI

In the landscape of high-scale AI, many architects fall into the “RAM Trap”: the expensive conviction that a billion-vector search system requires a professional-grade server cluster groaning under te...

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Claude Skills API vs Tool Use: How Developers Should Choose the Right Extension Layer

 Towards AI

Production AI apps need an extension stack, not a pile of interchangeable agent features. Skills, tools, MCP connectors, browser use, and Files API all extend an AI system. They do not solve the same ...

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How to Pick a Local Model in 2026: Five Rules, and Benchmarks Aren’t One

 Towards AI

Seven models got the same small task. The one built for coding never touched the file. Continue reading on Towards AI

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Prompt, Context, Graph, Harness: The Way We Talk to LLMs Keeps Changing

 Towards AI

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 Towards AI

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NVIDIA's New Agent Framework Turned 10 of My 20 Python Method Bodies Into AI Agents

 Towards AI

The rule is supposed to be simple: a body of ... means the LLM writes it. I wrote twenty bodies to find the edge of that rule, and then I… Continue reading on Towards AI

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AI Agents: From Chatbots to Autonomous Systems in 2026

 Towards AI

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The SRE series: Agentic AI Is Powerful, But Most Teams Use It Wrong

 Towards AI

The Hard Truth About Agentic AI in Production Continue reading on Towards AI

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Build Your Own Coding Agent in 30 Minutes with Snowflake’s New code_toolset_all

 Towards AI

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Beyond Bugs: An Engineering Guide to Strategic Application Testing with Python

 Towards AI

Executive Summary: The Strategic Value of Testing Continue reading on Towards AI

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