How community banks are modernizing their core systems, unlocking AI readiness, and preparing for the agentic era with Workato and API People.
Community banks and credit unions have long served as anchors of local economies, yet many find themselves constrained by decades-old technology. As the pace of digital transformation accelerates, these institutions face a critical question: how do you evolve from maintaining legacy systems to building intelligent, orchestrated enterprises?
To unpack this, we sat down with Steve Scott, Founder and CEO of API People, a long-time Workato partner helping financial institutions modernize their technology foundations. Steve shared candid insights on the realities of banking core modernization, the role of orchestration in enabling AI, and the steps banking leaders can take today to prepare for an agentic future.
The Modernization Challenge: Breaking Free from Banking Cores
“Most community FIs are still running on systems built in the 80s or 90s,” Steve begins.
At the center of nearly every regional bank and credit union is a banking core system: a mainframe built before modern APIs existed, often running on primitive databases with limited extensibility. Over the years, many vendors have bolted on XML or SOAP APIs, but documentation remains scarce, and integration support is minimal.
This creates a technical dependency loop. When internal teams lack the resources or expertise to work with these cores, they’re forced to purchase “compatible” products directly from the banking core provider. These solutions might be convenient, but they often aren’t best-of-breed.
“Over time, this increases the FI’s reliance on the core vendor and makes it increasingly difficult for them to take ownership of their technology stack,” Steve explains. “Without a technically confident CIO or modern IT team, transformation just stalls.”
The result is a widening gap between the digital experiences customers expect and what legacy infrastructure can deliver.
The AI Adoption Paradox
For many financial leaders, AI adoption has become both a goal and a source of hesitation.
“I don’t see most clients pushing real-time AI internally yet,” Steve notes. “There are still concerns around data privacy, transparency, and regulatory auditability.”
Those concerns are justified. Legacy systems with limited APIs and no access to real-time events make AI integration difficult, not because the technology isn’t powerful, but because the underlying data architecture isn’t ready.
While some institutions have turned to RPA (robotic process automation) to automate specific back-office tasks, progress often plateaus there.
“Legacy banking cores keep banks and credit unions trapped in a reactive mode,” Steve says. “They can adopt surface-level automation, maybe a chatbot or a reconciliation bot, but not the deeper, data-driven AI that improves decision-making or customer experience.”
The shift to agentic AI, intelligent systems that can take action rather than just generate insights, requires an intermediate step: orchestration.
Orchestration: The Foundation of Agentic AI
“To move from Generative AI to Agentic AI, where an agent can autonomously take actions, there needs to be a foundation in place for that agent to communicate with,” Steve explains.
That foundation is an orchestration layer, the connective tissue that bridges legacy systems and modern AI capabilities. For institutions still bound to decades-old cores, orchestration serves two critical functions:
Integration: By exposing modern APIs or MCP (Model Context Protocol) servers, orchestration makes it possible for AI agents to interact with otherwise isolated systems.
Governance: Orchestration abstracts and monitors interactions to ensure that every action is secure, compliant, and auditable.
“A concern for FIs is always going to be the compliance and auditing aspects of AI,” Steve adds. “An abstracted orchestration layer lets you govern and audit actions through the middleware layer, so you can enable automation safely.”
In practice, this means orchestration doesn’t just connect systems. It creates a controlled environment where AI can operate within defined boundaries. It’s the guardrail and the bridge that make agentic AI viable for highly regulated industries like banking.
The API People Approach: Beyond Integration
Founded on a philosophy of integrity, technical excellence, and long-term partnership, API People approaches banking transformation differently.
“We live our core values,” Steve says. “We lead with honesty, act with integrity, and work tirelessly in pursuit of excellence for every client we serve. Our commitment doesn’t end at 5 p.m. We stay responsive, available, and dedicated to helping whenever and wherever we’re needed.”
That mindset is matched by deep domain expertise. API People has delivered automation and orchestration projects across multiple banking core platforms, maintaining a strong technical partnership with Jack Henry while also collaborating with Fiserv and FIS environments.
This cross-core fluency allows the team to design architectures that are both scalable and future-ready, regardless of a client’s starting point.
“We’re not just integration specialists,” Steve emphasizes. “We operate as an extension of the institution’s technology team, helping design and deliver architectures that are scalable, secure, and built for the future.”
From Automation to Autonomy: The Agentic Opportunity
What lies ahead for banks that embrace orchestration today? Steve sees enormous potential.
“As institutions modernize their APIs, those same interfaces can evolve to become MCP-enabled, exposing capabilities that AI agents can discover and use,” he explains. “What begins as integration work today becomes the foundation for AI interoperability tomorrow.”
He envisions two classes of agents emerging within the next generation of financial institutions:
- Autonomous Agents
These agents complete fully automated, rules-based tasks such as reconciling transactions, processing reports, or initiating service workflows, all within defined audit and compliance boundaries. They free staff from repetitive, time-consuming work and create consistent operational accuracy. - Interactive Agents
These agents collaborate with people, surfacing insights and preparing actions for review. Imagine a loan officer receiving a pre-qualified opportunity generated by an AI agent recognizing deposit growth trends, or a fraud analyst alerted to anomalies before they escalate.
Together, these agents, or Genies, represent the shift from automation to autonomy, where AI doesn’t just inform human decision-making but meaningfully extends institutional capacity.
“The long game here is composability,” Steve concludes. “Banks that build orchestrated, API-driven foundations today will be ready for agentic ecosystems as simple or as complex as they’re comfortable managing.”
Banking for the Agentic Era
For banking leaders ready to take the first step, Steve’s advice is pragmatic: start with governance and exposure.
“Take this seriously,” he urges. “Understand where your data is, how it’s structured, and how it can be exposed securely. Build the orchestration layer that will allow AI agents to access and act on that data within compliant boundaries.”
In other words, agentic readiness isn’t about deploying AI tomorrow. It’s about preparing your data and systems today. By investing in orchestration, banks and credit unions can turn their technology stack from a bottleneck into a launchpad.
And when the shift toward AI-driven operations inevitably accelerates, those institutions will already have the rails in place.
Learn More
To explore how financial institutions are modernizing their core banking systems, building orchestration layers, and preparing for the next wave of intelligent automation, visit Workato’s Financial Services page.