The article titled "What's in the Box? Distinguishing Acceptable Traces from Unacceptable Ones" discusses how to transform scattered requirements into deployable agents. It focuses on identifying which traces are acceptable and which are not for effective deployment. The piece was originally published on Towards Data Science.
The article titled "Your Agent Doesn't Need More Freedom" discusses how agents have become proficient, but the real challenge lies in the surrounding environment. It highlights that the focus should shift from granting agents additional autonomy to addressing the complexities that exist around them. The piece underscores the importance of improving the context in which agents operate rather than simply increasing their freedom.
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 .
The article "AI Harms and Their Technical Causes" discusses how specific technical decisions in AI development can result in safety failures. It examines the underlying causes of these harms and offers guidance on alternative approaches to mitigate risks. The piece highlights the importance of thoughtful design choices in preventing unintended negative outcomes from AI systems.
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .
The article explains three methods for comprehending a codebase when coding agents generate code. It covers using Claude Code to produce feature memos, reviewing pull requests, and creating visual HTML explainers. These techniques help developers grasp what the agents deliver.
Enterprise Document Intelligence [Vol. 1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.
The article "8 Tips for Writing Effective Agent Instructions" offers quick and simple guidance to improve the clarity and effectiveness of instructions given to agents. It presents a concise set of practical tips aimed at helping readers craft better agent instructions. The post is published on Towards Data Science.
Multi-agent coding systems don't necessarily fail because agents can't communicate. They can fail because important commitments made in conversation have nowhere to live afterward. The post Multi-Agen...