Why most agents are just flowcharts in disguise, and what to build instead.

The post Is Agentic AI Just Automation? appeared first on Towards Data Science.

Understanding Codex hooks

The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.

How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

A lightweight runtime layer that separates instructions, evidence, memory, and tool output before they reach the model

The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.

28 debugging experiments reveal that AI struggles less with complexity than with missing information.

The post Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond) appeared first on Towards Data Science.

Turning a demo agent into something that can keep real booking data

The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.

Turning Codex from an interactive assistant into a programmable automation component

The post Running Codex as a Headless Agent appeared first on Towards Data Science.

Understanding Codex hooks

The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.

Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model. This article walks through the implementation, tests, and what this approach does—and does not—guarantee.

The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.

28 debugging experiments reveal that AI struggles less with complexity than with missing information.

The post Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond) appeared first on Towards Data Science.

Turning a demo agent into something that can keep real booking data

The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.

Turning Codex from an interactive assistant into a programmable automation component

The post Running Codex as a Headless Agent appeared first on Towards Data Science.

Building the Responsible AI, security, and governance layers required for enterprise-ready agents

The post From Prototype to Production: The Architecture Behind Secure & Governed AI Agents appeared first on Towards Data Science.

5 principles that determine whether an agent system succeeds in production, explained through one I built for a $100M+ company.

The post Building Enterprise Agent Systems that People can Trust, Verify and Improve appeared first on Towards Data Science.

For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And instead of leaving behind a click trace, it leaves something you can actually use again: a command-line tool.

The post Webwright: Why AI Web Agents Should Write Code, Not Click appeared first on Towards Data Science.

How autonomous agents broke two decades of capacity planning — and what to build instead

The post Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them appeared first on Towards Data Science.

Learn how to run OpenClaw bots for increased productivity

The post How to Orchestrate a Fleet of OpenClaw Bots appeared first on Towards Data Science.

A practical guide to choose the proper tool for your agentic workflows and systems

The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science.

I replayed the same 27 real production tasks through two local models, one hardware upgrade apart, to find out what it actually takes to replace Claude as the brain behind a 90-tool personal agent.

The post Can a Local LLM Run My AI Assistant? appeared first on Towards Data Science.