Towards Data Science
Jul 10
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 .
Why Adding More AI Agents Made Our System Slower
The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science .
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Towards Data Science
Jul 8
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 .
Towards Data Science
2d ago
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 .
Towards Data Science
Jul 31
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.
Towards Data Science
Jun 11
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 .
Towards Data Science
Jul 12
How to Orchestrate 100+ Agents With Claude Code
Run 100+ agents in parallel The post How to Orchestrate 100+ Agents With Claude Code appeared first on Towards Data Science .
arXiv AI
Aug 6
Architectural Implications of Agentic AI Workflows
arXiv:2608. 04458v1 Announce Type: new Abstract: Agentic AI is emerging in datacenters, but its architectural implications remain unexplored.
Towards Data Science
Jun 9
The Hardware That Makes AI Possible
CPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science .
arXiv AI
Jun 16
Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Ranking
arXiv:2603. 22376v2 Announce Type: replace-cross Abstract: We present an AI Co-Scientist framework that closes the research loop for the production search-ranking system of a large online travel platform -- pairing LLM agents with direct cloud-compute access so that idea generation, code implementation, GPU experimentation, and result analysis iterate end-to-end with a human scientist in the loop.
Towards Data Science
2d ago
Webwright: Why AI Web Agents Should Write Code, Not Click
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.
arXiv AI
1d ago
From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems
arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.
Towards Data Science
Jul 31
The 3× Token Bill We Didn’t See Coming
How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science .