In the digital economy, enterprises—especially banks and financial institutions—are becoming fully software-driven, where change is the new normal. Yet, most still operate on rigid legacy architectures, fragmented toolchains, and manual SDLC (Software Development Life Cycle) processes. The industry needs continuous improvements to keep up with regulations, cyber risks, and rapid product innovations. However, challenges remain where legacy applications are costly to replace, late-stage defects are 100x more expensive to fix, and scarce talent hinders transformation.

McKinsey highlights that AI agents represent the next frontier, moving from knowledge to action, capable of handling complex, multi-step workflows once too variable to automate. Proven implementations, such a bank’s migration from IBM mainframe to a distributed cloud-native core, shows that AI-assisted business modeling with integrated toolchains can transform core systems incrementally - mitigating risks while delivering measurable ROI.

Today’s key pain-points for the traditional SDLC are:

The GienTech SDLC AI Agents modernize enterprise software delivery by embedding intelligence across the full SDLC. It can be modularly implemented depending on the client’s specific needs.

Powered by the Business-Technology Integrated (BTI) Toolchain, it provides a unified platform where business models drive design, coding, testing, and deployment—ensuring end-to-end traceability and alignment. It includes a proven business modeling methodology and a banking reference model.

Market solutions focus mainly on the code level, whilst our model-driven, business-technology integrated approach ensures end-to-end traceability — from requirements to models, then code, and tests — uniquely aligning delivery with banking compliance and business needs.

BTI Toolchain Key Capabilities

Model-driven automation: Business needs, requirements, and even legacy code are transformed into a centralized model repository, serving as the single source of truth for reqs-to-mode, code-to-mode, model-to-test, model-to-code and model-to-deployment.

AI agent integration: Automates coding, testing, governance, and model generation with human-in-the-loop while maintaining compliance and traceability.

Unified SDLC pipeline: Seamlessly links DevSecOps, runtime platforms (PaaS), and infrastructure (IaaS) to enable faster, more agile, and cost-efficient delivery.

Specialized AI Agent Solutions

AI Modeling Agent: Converts unstructured requirements or legacy code into structured business models, enabling requirement-to-model and code-to-model capabilities.

AI Coding Agent: Generates up to 90% of production-ready code from validated models or requirements with human-in-the-loop review. It supports model-to-code and requirement-to-code and includes an executable model capability for directly running modeled business processes and rules without generating code.

AI Testing Agent: Shifts testing left by auto-generating test cases, scripts, and data—cutting defect resolution time by up to 50%, with model-to-test and requirement-to-test coverage.

Together, these agents shorten the delivery cycles, enable shift-left testing, and support incremental modernization strategies such as the strangler pattern, reducing risks of big-bang cutovers.

For banks, this delivers modern, cloud-native, distributed, and business-driven core systems with resilience and agility.

Figure 1: Continuous SDLC powered by BTI Toolchain

Figure 2: AI Agent Solution Accelerator Suite