# Goldman Sachs Expands AI-Agent Skills Across Junior Roles

By Simon Yoon

Canonical URL: https://www.tokenpost.com/news/technology/27786
Published: 2026-10-08T06:31:06.000Z
Updated: 2026-10-08T06:31:06.000Z
Section: Technology

> The bank’s AI operating model covers onboarding, regulatory reporting, lending and risk management as experienced-hire applications exceeded 1.1 million in 2025.

Goldman Sachs is expanding the AI-related skills expected of junior employees as it reorganizes internal workflows, a shift that could influence staffing and oversight practices across regulated financial markets.

The bank’s One Goldman Sachs 3.0 operating model, announced in 2025, applies artificial intelligence to client onboarding and know-your-customer processes, vendor management, regulatory reporting, lending, enterprise risk management and sales enablement.

Goldman Sachs’ public materials do not confirm that new hires will manage AI agents from day one. Goldman Sachs Chief Information Officer Marco Argenti has described a broader change in which junior employees and individual contributors learn to define tasks, delegate work to AI agents and review their output.

“This shift will require even the most junior employees and individual contributors to master three foundational management skills: Describing a task clearly, delegating it effectively to an AI agent, and supervising the results,” Argenti wrote.

He also emphasized that human review remains necessary when agents handle work. “Delegating work to an agent without the ability to supervise it is a recipe for disaster,” Argenti wrote, warning that AI systems can produce incorrect or dangerous actions.

The comments come as Goldman Sachs reports more than 1.1 million experienced-hire applications in 2025 and a summer internship selection rate below 1%.

Goldman Sachs has also discussed AI software-engineering tools, including a 2025 conversation involving Argenti and Russell Kaplan, president of Cognition, about Devin, Cognition’s AI software engineer. The discussion illustrates the bank’s interest in AI development tools but does not establish broad deployment across its engineering workforce.

For financial and crypto-market companies, the operating model points to practical changes in compliance controls, client onboarding and the infrastructure supporting regulated activity. Goldman Sachs’ stated priorities include increasing operational capacity, improving data quality and keeping human employees responsible for AI-enabled processes.