Goldman Sachs has quietly become one of the most concrete case studies in enterprise agentic AI, and the details reported by Forbes this week are worth more than the usual “bank adopts AI” headline. Hundreds of autonomous coding agents — Cognition’s Devin and, more recently, Anthropic’s Claude — are now working alongside roughly 12,000 human engineers on real production tasks, not pilots.
Here’s what Goldman actually did, what’s working, and where the real friction is showing up.
Why a Bank Needed This in the First Place
The financial services industry runs on infrastructure that’s often decades old — systems built by engineers who have since retired, in languages that have fallen out of common use. Modernizing that legacy stack is exactly the kind of expensive, error-prone, repetitive work that human engineers hate and that AI agents, notably, don’t have opinions about.
That gap was Goldman’s entry point. In 2025, the bank deployed Devin, developed by AI startup Cognition, across its technology division — not as a code-completion assistant, but as a fully agentic system capable of scoping a project’s requirements, writing the code, testing it, submitting it for human review, and correcting its own bugs.
The Numbers Behind the Headlines
Goldman’s CIO Marco Argenti has described Devin to CNBC as being “like a new employee,” expecting roughly three to four times the productivity of the AI tools that came before it. Goldman hasn’t published its own granular internal statistics, but a late-2025 performance review from Cognition covering its broader customer base gives a sense of the trajectory: one large organization saved 5–10% of development time using Devin for security-issue fixes, while another saw per-issue vulnerability fix time drop from 30 minutes to 1.5 minutes compared to human developers. Pull requests accepted by human reviewers without significant recoding rose from roughly a third to about two-thirds over that period as the tool’s performance improved.
Encouraged by those results, Goldman expanded its agentic engineering program in 2026, layering in Anthropic’s Claude for trades and transactions processing as well as client vetting and onboarding — domains well outside the original legacy-modernization use case.
Argenti has since told Fortune that he’s shifted how he measures success internally: rather than tracking AI usage among staff, he’s watching how quickly ideas move from prototype to working production systems. He described the effect as the bank having become capable of “3D printing software.”
Augmentation or Replacement? Goldman Isn’t Fully Answering
Argenti has consistently framed agentic automation as freeing humans for higher-value work rather than replacing them outright. But the broader numbers around it are harder to wave away. Bloomberg Intelligence research cited in the Forbes piece estimates up to 200,000 job losses across the US banking sector as AI erodes roles — a substantial share concentrated in junior-level development positions, the exact tier of work Devin and Claude are now absorbing.
That creates a real structural tension: junior engineering work has historically been how senior engineers get made. If AI agents handle a growing share of the “learn by doing the tedious parts” tasks, banks — and really any industry deploying agentic AI at this scale — need an answer for where the next generation of senior talent comes from. Goldman, notably, doesn’t have a public answer to that question yet. CEO David Solomon has talked about restricting headcount, and Argenti himself has said he expects AI-driven cuts to extend into departments beyond engineering.
The Actual Lessons, Distilled
Strip away the headline numbers and Goldman’s rollout offers a genuinely reusable playbook for any organization considering agentic AI at scale:
- Start narrow, with a problem you can measure. Legacy code modernization was a known, bounded problem where success or failure would be obvious quickly. That let Goldman build internal confidence before expanding into trades, transactions, and client onboarding.
- Measure outcomes, not usage. Argenti’s shift from tracking adoption metrics to tracking time-to-production is the more instructive signal here than any specific productivity multiplier.
- Expect agents to be senior-level on understanding, junior-level on execution. Cognition’s own review found Devin performs at a senior level when reasoning about existing code, but more junior when objectives are ambiguous — which puts the burden back on humans to write well-scoped, structured prompts and task definitions.
- Don’t pretend the workforce question is solved. The junior-talent pipeline concern isn’t unique to banking, and ignoring it now is a bet that this generation of engineers can be trained some other way later.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260807-0800
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