How AI Agents Are Reshaping Financial Services

By Albert Geisler Fox, CEO at Performativ

Across wealth management, asset management, and banking, AI is showing up in very practical places. Not as a single program or transformation initiative, but inside day-to-day processes that support advisors, analysts, and operations teams.

What we consistently see, however, is a gap between experimentation and production. Many institutions have proven that AI can be useful in isolation. Far fewer have been able to deploy it in ways that survive regulatory scrutiny, data complexity, and real operational scale.

This is where the conversation is starting to change.

Rather than asking what AI can do, firms are increasingly asking what AI can do inside their existing constraints. That shift is driving interest in agentic workflows.

Agentic workflows move beyond individual tasks and instead coordinate actions across systems, data, and rules. For regulated financial entities, the appeal is straightforward: AI that works the way the business actually works.

Why Generic Models Rarely Make It to Production

Generic AI models are impressive. They are also blunt instruments.

In practice, they tend to break down when exposed to MiFID requirements, internal risk frameworks, data residency rules, and approval hierarchies. This is why many AI pilots never leave the lab. The technology works, but it does not fit the operating reality of a bank or wealth manager.

For AI to deliver sustained value, it must run inside the firm’s governance perimeter. It must integrate with core systems, apply policies deterministically, and produce outputs that can be reviewed, audited, and explained.

That is the problem Custom Agents are designed to solve.

Building Agents That Understand Your Environment

Performativ enables financial institutions to build custom AI agents directly on top of their existing infrastructure. Through a native Model Context Protocol (MCP) server and a production-proven integration with OpenAI AgentKit, agents can interact with core banking systems, CRMs, portfolio platforms, and internal document stores.

This is not about replacing systems. It is about connecting them.

Data residency and privacy are enforced by design. No personally identifiable information needs to leave the tenant, and access is governed by scoped permissions and approvals. Teams can experiment, refine, and deploy agents quickly, while retaining full visibility into how they behave.

In our experience, this combination of speed and control is what determines whether AI becomes operational or remains theoretical.

A Practical Example: Turning Idle Cash into Actionable Opportunities

To make this concrete, consider a simplified banking scenario.

A custom agent monitors client cash balances and identifies accounts with unusually high idle cash or term deposits approaching maturity. When triggered, the agent reviews CRM notes to understand suitability, life goals, and recent interactions. It retrieves the latest product materials from internal PDFs, validates MiFID constraints and house risk views, and assesses whether a suitable investment opportunity exists.

If the criteria are met, the agent assembles the relevant materials and creates a qualified opportunity in the external CRM for an advisor to review.

The important detail is not the workflow itself, but the control around it. Policies, disclosures, and approvals govern every step. Advisors remain accountable for decisions, while the agent removes the friction that slows preparation and follow-up.

Outcomes Institutions Actually Care About

When agentic workflows are deployed in production, the results tend to be practical:

• Faster revenue capture through timely, compliant outreach

• Lower operational overhead from automated triage and follow-ups

• Better oversight through audit trails, approvals, and policy checks

• More consistent client experiences across teams and products

  
  

These patterns repeat across functions. Commercial teams surface renewal and upsell opportunities earlier. Advisors identify misalignment between portfolios and client intent. Risk and compliance teams automate monitoring and exception handling. Operations teams reduce manual workload without adding headcount.

Guardrails Are the Difference Between AI and Risk

One thing is non-negotiable: agents cannot run unsupervised.

Performativ provides deterministic policies, approval workflows, least-privilege access, and full observability. Teams can replay agent decisions, understand why actions were taken, and adjust behavior as requirements evolve.

These controls extend beyond regulatory compliance to include market, balance sheet, and operational limits. The platform undergoes regular third-party audits and today supports both large enterprises and specialized wealth and asset managers.

Moving from Proof to Production

AI only creates value when it understands your data, rules, and clients. Custom Agents make that practical. They operate inside existing systems, respect governance by default, and deliver outcomes that can be measured.

From our perspective, the question is not whether AI belongs in wealth and asset management. It is whether institutions are prepared to move beyond experimentation and put it to work, responsibly, at scale.