Analysis of enterprise deployments finds 88% of AI agents never reach production, with infrastructure gaps the leading cause at 41%, ahead of governance and security at 38%. A March 2026 survey of 650 enterprise technology leaders found 78% running agent pilots and only 14% scaled to production. The models are capable and the tooling has improved, the problem is your infrastructure. Here’s why a core built only for human use cannot carry an agent and what a core has to provide instead. 

Why does your core decide whether an agent works? 

Most banking cores were designed around people-led, screen-based, batch-processed operations, because that is what banking looked like when they were built. Every assumption baked into that design, like that a person initiates each action, that data settles overnight, and that the interface is where work happens, is an assumption the use of agents breaks. 

It is why so many agentic pilots die after the demo. Prototypes work in clean sandboxes and then lack the protocols to integrate with the live enterprise systems around them, a pattern researchers have started calling innovation theatre. The demo runs against a convenient copy of the data and a narrow slice of the workflow. Production needs the agent to read the real book and act on the real core, and the core was never built to be driven by software. So the pilot ends up failing because it meets your core. 

An AI-native core provides three things a core built for screens cannot. Data an agent can read safely, in real time. Complete API coverage and real-time events so the agent can act and know when to act. And a governed execution layer so its actions stay auditable.  

It starts with data the agent can trust 

An agent reasoning about a customer needs a complete, current picture, and it needs the context around the data as much as the data itself. The most common reason agentic projects stall is building agent logic before the data foundation is in place, because agents need lineage, business logic and quality history, not just raw fields. A core that surfaces data hours or days late, spread across systems that have to be reconciled, gives an agent a partial and stale view to reason from, and the institution cannot trust a decision made on that basis. 

What an agent needs is a governed, real-time view of the whole book that it can query freely without loading the production system. When the data foundation is current, complete and safe to read, every use case above it becomes possible. When it is not, the models never leave the lab, whatever their quality. 

Then the agent has to be able to act 

Reading is half of it. To move from an assistant that suggests to an agent that acts, the core has to let software do what a person would do through a screen, and that needs two properties most legacy cores lack. 

The first is complete API coverage. Every function of the core has to be reachable through a stable, documented API, because partial coverage, where the easy reads are exposed and the consequential actions stay locked behind the interface, leaves an agent able to see everything and change nothing.  

The second is real-time event streams. An agent has to know the moment something happens, a payment settling, a balance crossing a threshold, a loan maturing, so it can act on the event as it occurs instead of discovering it in the next batch. Complete coverage lets an agent finish a task end to end. Real-time events let it act at the right moment. Without both, agentic banking stays a slide deck. 

It has to stay governed 

Governance and security are the second most cited cause of agentic failure for good reason. An agent acting autonomously is a system the board and the regulator will only approve if the institution can show what it did, on what data, under what logic, with human oversight at the points that matter. A core that was not built to be governed cannot produce that record, so its agents stay stuck in pilot no matter how well they perform. 

This is where the same architecture pays twice. A governed data foundation and an owned, versioned, auditable execution layer give a complete account of every automated decision, which is what turns an impressive pilot into a system a risk officer can sign off. The infrastructure that makes an agent capable is the infrastructure that makes it approvable, and in regulated banking the second matters as much as the first. 

Where this leaves institutions in emerging markets 

The direction of travel is not in question. Gartner expects 40% of enterprise applications to embed task-specific agents by the end of 2026, up from under 5% a year earlier, while forecasting that more than 40% of agentic AI projects will be cancelled by 2027 on cost, unclear value or weak governance.  

For banks and lenders in emerging markets, activities such as assessing a thin-file borrower in real time, catching a deteriorating loan before it defaults, and matching products to customers across the whole book, is exactly the agentic work a legacy core cannot support. Oradian is built as an AI-native, API-first core for these markets, with a governed data foundation, complete API coverage and real-time events, and a safe execution layer for institution-owned logic, so an agent has data it can trust, a way to act, and a record of everything it did. The model is available to everyone. A core an agent can act inside is the part that has to be built. 

Want to put AI into production? 

Ready to leverage an AI-ready, API-first core designed to enable you to ship faster, grow your client base quicker, and reduce governance risks in the modern era? Contact vanda.jirasek@oradian.com to book a demo of Oradian.