Agentic Resource Discovery: Let agents search

Agentic RAG: Let the Agent Search

A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .

Agentic Search. More accurate and efficient results from your AI systems.

The retrieval layer that helps AI systems navigate, read, and verify information inside even the most complex documents

Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems

arXiv:2505. 21550v2 Announce Type: replace-cross Abstract: Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments.

Build AI agents with the Mistral Agents API

Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.

BrowseComp: a benchmark for browsing agents

BrowseComp: a benchmark for browsing agents.

Computer-Using Agent

How V7 gives AI agents institutional memory

Using GPT-5.6, V7 turns scattered company files into context agents can use to complete complex, source-linked work.

The Protocol That Cleaned Up Our Agent Architecture

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 .

Upgrading agentic coding capabilities with the new Devstral models

Where the Agent Development Lifecycle Fits

The article "Where the Agent Development Lifecycle Fits" discusses how to coordinate the development of agent capabilities with the applications they power. It highlights the importance of aligning agent creation processes with the needs of the end‑use cases they support. The piece appears on Towards Data Science and focuses on integrating agent development into broader application workflows.

Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search

Project Greenhouse explores building fully open and sovereign agentic search models with modest computational resources. The authors present a competitive pointwise decoder-only reranker trained from scratch and fine-tuned on common datasets, avoiding reliance on third‑party open‑weight backbones. They demonstrate that most experiments can be run on a handful of GPUs and release data, code, configurations, and checkpoints for reproducibility.