Goldman Sachs is pushing deeper into real use of artificial intelligence inside its operations, moving to systems that can carry out complex tasks on their own. The Wall Street bank is working with AI startup Anthropic to create autonomous AI agents powered by Anthropic’s Claude model that can handle work that used to require large teams of people. The bank’s chief information officer says the technology has surprised staff with how capable it can be.
Many companies use AI for tasks like helping employees draft text or analysing trends. But Goldman Sachs is testing AI systems that go into what bankers call back-office work – functions like accounting, compliance checks and onboarding new clients – areas viewed as too complex for automation. Such jobs involve many rules, data and detailed review, and have resisted full automation.
Moving AI agents into process-heavy operations
The partnership with Anthropic has been underway for roughly six months, with engineers from the AI startup embedded directly with teams at Goldman Sachs to build these agents side by side with in-house staff, according to a report based on an interview with the bank’s CIO. The work has focused on areas where automation could cut the time it takes to complete repetitive and data-heavy tasks.
Marco Argenti, Goldman’s chief information officer, described the AI systems as a new kind of digital assistant. “Think of it as a digital co-worker for many of the professions in the firm that are scaled, complex and very process-intensive,” he told CNBC. In early tests, the ability to reason through multi-step work and apply logic to complex areas like accounting and compliance was something the bank had not expected from the model.
Goldman Sachs has been among the more active banks in testing AI tools over the past few years. Before this announcement, the firm deployed internal tools to help engineers write and debug code. But the change now is toward systems that can take on work traditionally done by accountants and compliance teams. That highlights how organisations are trying to find concrete business uses for AI beyond the hype.
Faster workflows, human oversight remains
The agents are based on Anthropic’s Claude Opus 4.6 model, which has been built to handle long documents and complex reasoning. Goldman’s tests have shown that such systems can reduce the time needed for tasks like client onboarding, trade reconciliation and document review. While the bank has not shared specific performance numbers, people familiar with the matter told news outlets that work which once took a great deal of human labour can now be done in much less time.
Argenti said the rollout is not about replacing human workers, at least not at this stage. The bank reportedly views the agents as a tool to help existing staff manage busy schedules and get through high volumes of work. In areas like compliance and accounting, jobs can involve repetitive, rule-based steps. AI frees analysts from that repetition so they can focus on higher-value judgement work.
Markets have already reacted to the idea that large institutions are moving toward more AI-driven automation. In recent days, a sell-off in enterprise software stocks wiped out billions in value as some investors worried that tools like autonomous agents could speed up the decline of traditional business software that has dominated corporate IT for years.
AI adoption meets governance reality
Industry watchers see Goldman’s move as part of a wider trend. For example, some firms are piloting tools to read large data sets, interpret multiple sources of information, and draft investment analysis. These steps show AI making the jump from isolated projects to operational work. Yet the technology raises questions about oversight and trust. AI systems that interpret financial rules and compliance standards must be monitored carefully to avoid errors that could have regulatory or financial consequences. That’s why many institutions treat these systems as helpers that are reviewed by human experts until they mature.
Goldman Sachs is starting with operational functions that have traditionally resisted automation because they involve a lot of data and formal steps. The bank has not said when it expects deployment of the agents in its operations, but executives have suggested that the initial tests have been promising enough to support further rollout.
The broader industry context shows other banks and financial firms also exploring similar use cases. Some have already invested heavily in AI infrastructure, and reports indicate that major firms are planning to use AI to cut costs, speed workflows and improve risk management. However, many remain cautious about putting AI into customer-facing or regulated functions.
Goldman’s push into autonomous AI agents is an example of how large companies are reshaping internal operations using the latest generation of AI models. If systems can handle complex tasks reliably, organisations could see real changes in how work gets done – particularly in back-office functions where volume and repetition keep costs high and innovation slow.
(Photo by Louis Droege)
See also: Intuit, Uber, and State Farm trial AI agents inside enterprise workflows
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