Overview
- Major retail and commercial bank
- Assets: $900B+
- Branches: 500+
Challenge
The institution wanted to explore no-code AI agents to speed up workflow automation, but faced a familiar tension: innovation moving faster than governance. Compliance, security, and integration risks were slowing adoption and blocking scale.
Solution
Using NayaOne’s sandbox, the bank tested a leading low-code platform against realistic enterprise conditions. End-to-end workflows were built with synthetic data and simulated APIs, while teams measured build time, governance setup, and integration fidelity.
Results
- Proved agentic workflows feasible
- Surfaced risks early
- Delivered workflows in 4 weeks
- Informed adoption decisions
- Engineering & DevOps (incl. Architects)
- Cybersecurity & Risk
- Compliance & Audit
- Chief Data & Analytics Office (CDAO)
The proof of concept explored a set of modern no-code and low-code AI agent platforms that promise to accelerate automation in financial services. Each technology was deployed inside a secure sandbox environment designed to replicate enterprise conditions without the risk of exposing live systems.
Simulated APIs, synthetic customer and transaction data, and enterprise-like test systems provided a realistic foundation for testing how these platforms would perform in production – while keeping governance and security requirements front of mind.
The Tension: Speed vs. Governance
Financial institutions face constant tension between innovation and governance. While AI agents promise faster delivery of automated workflows, adoption is constrained by:
- Compliance and audit requirements
- Integration fidelity with legacy systems
- Security and observability risks
Without a safe way to test these platforms, institutions risk either moving too slowly or deploying unproven technology at scale.
Building Real Workflows, Not Demos
Working within the sandbox, teams designed and deployed end-to-end workflows that mirrored live business scenarios. This allowed the institution to test the platforms not just for technical capability, but for enterprise readiness.
The evaluation measured build time, governance setup effort, quality of audit signals, and the ease of integrating APIs with core banking systems. Engineering and DevOps leaders worked alongside Cybersecurity, Risk, Compliance, Audit, and the CDAO function to ensure every angle was covered.
This cross-functional engagement was critical to assessing whether these platforms could deliver speed without compromising the standards expected in a regulated environment.
Four Weeks to Proof: What Worked, What Didn’t
The testing confirmed that all of the platforms could deliver basic automation, but only a few successfully balanced agility with the depth of controls needed by a large financial institution.
Agentic workflow construction was proven technically feasible across all vendors in just four weeks – a significant acceleration compared to traditional onboarding.
Importantly, the sandbox surfaced audit, data, and integration concerns early in the process, reducing enterprise risk and avoiding costly rework later. By providing consistent evaluation conditions, the sandbox enabled multi-stakeholder engagement and gave decision-makers clear evidence to move forward with confidence on platform selection.
