Towards Data Science By Abdullahi Dattijo

Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches?

Read the original on Towards Data Science →

The article examines whether an apartment search agent can reduce the number of times it calls a predictive model while still identifying suitable matches. The author conducted 2,500 listing checks using Weights & Biases Weave, systematically eliminating unnecessary model computations, and evaluated each iteration against consistent reference answers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

Towards Data Science
6d ago

Good Architecture Deletes the Signals Your Agent Depends On

The article argues that well‑designed architecture can inadvertently eliminate signals that tooling relies on, turning a structural issue into a search problem. It highlights how drawing boundaries in systems can strip away essential cues needed by agents. The piece emphasizes the importance of considering signal preservation when designing architecture.

By Yonatan Sason
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.

By Miodrag Cekikj