Agentic AI Accelerator for Enterprise Cloud Migration

Modernising Legacy Core Banking Systems

Migrating from On-Premises to Google Cloud

Built more than two decades ago, our customer’s core banking systems were tightly coupled and highly complex, weighed down by extensive third‑party dependencies, significant technical debt, and large volumes of unstructured and obsolete data. Modernising and migrating these systems to a GCP environment required overcoming substantial technical and operational challenges.

• Migration Complexity: A simple lift‑and‑shift migration approach could not be adopted as each legacy system required extensive code refactoring, dependency decoupling, data rationalisation, and environment re‑engineering before deployment to GCP.
• Vanilla Ecosystem: The target migration environment was provided as a fully vanilla cloud setup, with a strict mandate to rely exclusively on native cloud services to adhere to cost, security, and governance requirements.
• Delivery Risks: While our customer’s leadership had committed to setting up the cloud infrastructure and migrating the ten most critical core banking systems within a year, traditional migration approaches posed significant risks to delivery timelines and operational stability.
• Cloud Flexibility: A scalable, cloud-agnostic solution needed to be implemented to avoid dependencies on a single cloud provider like GCP, as future migrations across Azure, AWS, and/or other cloud platforms were anticipated as part of our customer’s long‑term digital transformation roadmap.

Full-Stack AI Engineering Capabilities

Specialised Expertise Meets Delivery Discipline

To bridge their capacity and capability gaps, our customer engaged TEKsystems’ expertise to build a cutting‑edge AI-enabled solution that would reduce manual workloads and seamlessly deliver this high-stakes cloud migration – on time, within budget, and with full compliance. We partnered with key programme stakeholders to design and deliver a custom agentic AI accelerator capable of supporting complex, end‑to‑end system migrations while remaining cloud‑agnostic by design.

To bring the initiative to life, TEKsystems deployed a high‑calibre squad comprising two Senior AI Engineers and four Full‑Stack Engineers, bringing deep expertise across Python, Gemini (LLM), agent‑based AI architectures, and public cloud platforms.

Building a Fit-for-Purpose Agentic AI Accelerator

Cloud‑Agnostic Scale and Repeatable Migration

Instead of building point‑in‑time migration tooling, TEKsystems’ squad engineered the platform infrastructure and agentic AI accelerator in a way that abstracted cloud‑specific services, allowing the same core framework to be reused regardless of the underlying cloud provider. This ensured that our customer could seamlessly evolve their cloud strategy without re‑architecting the migration platform each time.

To seamlessly execute the cloud migration, our squad designed a multi‑agent architecture, with each AI agent fit-for-purpose and owning a discrete phase of the migration lifecycle.

• Code Analysis Agent: Analysed legacy systems to identify refactoring requirements, technical debt, and structural issues prior to the migration.
• Code Refactoring Agent: Refactored and modernised system logic to improve maintainability, performance, and cloud readiness.
• Data Rationalisation Agent: Cleansed and restructured data by removing redundant records, decoupling dependencies, and eliminating obsolete libraries and artefacts.
• Migration Orchestration Agent: Provisioned baseline cloud infrastructure and orchestrated the controlled deployment of systems and data into the target cloud environment.

Post its successful pilot, the agentic AI accelerator enabled a four‑stage migration, enabling code transformation, data clean‑up, dependency rationalisation, and environment setup to run seamlessly. This approach was first applied to migrate core banking systems and subsequently extended to investment banking systems, demonstrating scalability across multiple banking domains.