Why Adding More AI Agents Made Our System Slower

The Big Con of Agentic AI

What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .

Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch

The article discusses how agentic AI is reshaping the analytics stack by taking over more execution tasks. It raises the question of which responsibilities should remain with human analysts versus AI agents and explores the importance of this distinction. The piece highlights the evolving role of AI in analytics and the need to define clear boundaries between human and machine work.

Redesign Work Before You Add More AI Agents

Map AI value, design workflows, redefine talent, upgrade the executive team, and measure the business impact. The post Redesign Work Before You Add More AI Agents appeared first on Towards Data Science .

Research acceleration: The view inside OpenAI

The article discusses how coding agents are transforming AI research within OpenAI. It presents early data on agent usage, experiment velocity, task complexity, and the resulting acceleration of research. The piece highlights the growing role of these agents in speeding up development and experimentation.

Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them

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 .

Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics

The paper investigates how large‑language‑model (LLM) based AI agents mix latency, local resource usage, and container bottlenecks when processing user requests that involve remote LLM calls and local tool execution. By measuring three representative tasks—retrieval‑augmented question answering, web search, and software coding—the authors show that agents exhibit diverse resource dynamics, with concurrent requests revealing task‑specific bottlenecks in CPU, disk I/O, and memory. Leveraging these insights, they propose CPU‑aware tool admission and task‑aware CPU allocation, achieving up to a 5.4× speed‑up for CPU‑sensitive tasks and a 32% reduction in average latency across multiple tasks.

AI Made Me 5x Faster. It Also Made Me 5x Worse at My Job.

One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code? The post AI Made Me 5x Faster. It Also Made Me 5x Wors...

Is Agentic AI Just Automation?

The article titled "Is Agentic AI Just Automation?" argues that many so‑called agents are merely flowcharts in disguise. It explains why this misconception exists and suggests what kinds of systems should be built instead to achieve true agentic AI.

AI Agents Are Here. What Now?

When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop

In five to ten years, the sharpest manager in your company might not be human, might not sleep, and might exist entirely in shared GPU memory. This is the systems-level view of the algorithmic corporation — why middle management collapses into a protocol, what breaks in the current AI stack, and what has to be built for autonomous agents to actually run a business.

When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI

Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .

AI Made Data Scientists Faster. Now It’s Expanding the Job.

AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.