How to manage AI investments in the agentic era

How Balyasny Asset Management built an AI research engine

By combining rigorous model evaluation, full-platform use of OpenAI, and agent workflows, Balyasny is reinventing investment research.

Netomi’s lessons for scaling agentic systems into the enterprise

How Netomi scales enterprise AI agents using GPT-4. 1 and GPT-5.

Practices for Governing Agentic AI Systems

From assistance to execution: How enterprises put AI to work

OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption.

AI Agents Are Here. What Now?

Q&A: What is agentic AI today, and what do we want it to be?

Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.

How enterprises are scaling AI

How enterprises scale AI: from early experiments to compounding impact through trust, governance, workflow design, and quality at scale.

Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions

The article reviews the emergence of Agentic AI, covering its evolution, theoretical foundations, working principles, and architectural aspects. It surveys recent scholarly contributions across various domains, highlighting real‑world applications, current research findings, and existing challenges. The review also proposes a framework for stakeholder adoption and outlines future research directions to guide researchers and practitioners.

AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality

How agents are transforming work

A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.

The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

The paper introduces an agentic strategic asset allocation pipeline called the Self Driving Portfolio, where 44 specialized agents generate market assumptions, 21 competing methods construct portfolios, and agents critique and vote on each other's outputs. A researcher agent can propose new construction methods, while a meta agent evaluates past forecasts against realized returns and rewrites agent code and prompts to enhance future performance. The entire process is governed by an Investment Policy Statement, mirroring the document that guides human portfolio managers, thereby constraining and directing autonomous agents.

Orchard: An open framework for scalable agentic AI

Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure.