Towards Data Science
Jun 3
What AI Agents Should Never Do on Their Own
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
Human-in-the-Loop Without Killing Throughput
The article explains how the authors eliminated the need to review every agent action by selectively directing human attention to the most critical moments. It describes the process of stopping exhaustive reviews and instead routing human oversight where it truly matters. The result is a more efficient workflow that maintains quality without sacrificing throughput.
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Towards Data Science
1d ago
We Specified an AI Agent in 2016. We Called It Analytics.
The article revisits the author’s 2016 IBM patent on real‑time concert feedback, framing it as an early example of an AI agent that combines multimodal perception, memory‑grounded inference, and human interaction. It reflects on what the patent captured correctly and how a decade of experience informs current agent design practices.
Towards Data Science
Aug 27
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.
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 .
Towards Data Science
Jul 23
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 .
Towards Data Science
Aug 26
How to Effectively Solve 100+ Tasks with Claude Code
The article "How to Effectively Solve 100+ Tasks with Claude Code" discusses strategies for working more efficiently with coding agents. It focuses on practical approaches to manage and complete a large number of tasks using Claude Code. The post was originally published on Towards Data Science.
Towards Data Science
Aug 14
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Enterprise Document Intelligence [Vol. 1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science .
Towards Data Science
Jun 7
We Should Train AI to Betray Its Users
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
Towards Data Science
Aug 27
Stop Giving Your AI Agent a Search Box and Start Giving It Typed Tools, Hard Bounds, and a Gate It Cannot Talk Past
The article explores the effects of removing a search box from an AI agent and instead providing it with typed tools, hard bounds, and a gate that it cannot bypass. It examines how the agent navigates a knowledge graph within strict limits and discusses findings from four models and one incorrect prediction regarding the value of this approach.
Towards Data Science
Jun 1
Escaping the Valley of Choice in BI
Why Agentic BI threatens an entire profession The post Escaping the Valley of Choice in BI appeared first on Towards Data Science .
Towards Data Science
4d ago
Where Does the Money Go Across Long-Running Coding Agents?
The article titled "Where Does the Money Go Across Long-Running Coding Agents?" explores the financial aspects of maintaining and operating coding agents over extended periods. It highlights the costs associated with controlling these agents and examines how resources are allocated during their lifecycle.
Towards Data Science
Sep 7
Why Most Multi-Agent Systems Fail Even When Evaluation Passes
The article explains how to detect a payload that appears correct yet is not, by employing a watchdog pattern in Python. It discusses the challenges that cause many multi‑agent systems to fail even when their evaluations succeed. The post was originally published on Towards Data Science.