StartupHub’s Daniel Singer this week published a deep dive into how foundation models and agentic AI are reshaping institutional finance. The article, "Wall Street's New Hires are Grok, Claude, and GPT," explores a fundamental shift happening across capital markets: the move from who can hire the most analysts to who can best encode judgment and proprietary expertise into agentic AI systems.

Auquan and our CEO Chandini Jain featured prominently in the piece, which examines how the financial services industry is standardizing around a two-layer architecture for AI deployment. At the base sits general foundation models that read, reason, and integrate data. On top, domain-specific agentic platforms like Auquan encode the judgment, risk frameworks, and audit requirements that institutional credit teams already trust.

The gap between AI Intelligence and execution

The article draws a sharp contrast between raw model capabilities and what actually works in high-stakes financial environments. The Nof1 Alpha Arena experiment demonstrated this gap vividly: multiple frontier models, including GPT, Claude, and Grok variants, were given capital to trade real order books. While some configurations showed promise, most burned through their accounts despite excelling on static benchmarks.

What this means is that generic intelligence doesn't equal trusted execution. High-stakes environments expose the difference between impressive capabilities and reliable performance. Finance teams need systems wrapped in high accuracy standards, tight constraints, audit trails, and domain-specific guardrails.

Credit markets show what adoption looks like when you get this right.

What credit teams actually need from AI?

In credit, the shift may feel less dramatic than algorithmic trading but it’s far more important for how institutions actually operate.. Credit teams don't want open-ended chatbots for highly repetitive mission critical workflows. They need systems that behave like experienced analysts following their own playbooks while leaving a traceable record of what was done and why.

Auquan's Credit Agent addresses this head-on. The system reads borrower financials and data room documents, extracts key fields and covenant details into structured formats, accesses subscription-based and public information, applies the firm's internal risk frameworks and scoring methodology, drafts investment committee memos in each firm's exact templates, and monitors covenant compliance with automated alerts. Large institutions use this to cut review times from days to hours on substantial credit portfolios and evaluate more deals without adding headcount.

As Chandini explained in the article: "We're not trying to replace how credit teams think. We're trying to turn the work they already do on messy borrower packs into something that runs end to end as software, without forcing them to change their process."

That distinction captures what makes domain-specific agentic AI valuable. It's not about generic intelligence. It's about how well a firm can encode its existing standards, risk culture, and decision frameworks into systems that operate at scale while maintaining the quality bar.

Encoding judgement at scale with Agentic AI

Foundation model labs are proving you can use general models for complex processes. But that's only half the equation.

"Labs are proving you can put a general model at the center of a workflow," Chandini noted. "But our job is to encode the judgment and checks that real credit teams already trust, so they can scale without lowering their standards."

Once that judgment is captured in software, the economics shift dramatically. The marginal cost of running another deal screen, another scenario analysis, or another monitoring pass drops to nearly zero. The bottleneck becomes the quality of the encoded logic rather than the number of human hours available.

This is the shift happening across institutional finance. Analysts move from manually building every spreadsheet and document to running and supervising automated processes, checking edge cases, resolving conflicts, and refining frameworks rather than retyping numbers between systems.

The new competitive advantage

The competitive advantage in finance is shifting. Historically, superior credit analysis was confined to firms with budgets for specialist teams and custom systems. Now platforms like Auquan productize underwriting and monitoring into repeatable, auditable systems that maintain institutional standards while operating at software speed.

The advantage goes to firms that best encode their judgment and proprietary data into these systems, then run them relentlessly. It's no longer just about hiring more people. It's about building better systems that capture what your best people already know.

Beyond credit

The pattern extends well beyond credit markets. Front, middle, and back office operations all center on large volumes of text and numbers, tacit institutional rules, and binding decisions. Each can be restructured into this same two-layer architecture: general foundation models plus vertical domain logic that encodes how that industry actually operates.

This is why model labs are pushing deeper into application and workflow tooling, and why domain-specific platforms like Auquan are becoming core infrastructure for how institutional finance actually works.

Read the full StartupHub article: Wall Street's New Hires are Grok, Claude, and GPT

Learn more about Auquan's Credit Agent: Visit here or schedule a demo to see how we're helping leading credit teams scale their underwriting and monitoring without compromising on standards.