This publication presents an established engineering-driven framework for pipelining large language model (LLM) agents for automatic code translation. The framework is applied in a long-term ongoing industrial project with Danfoss Power Solutions aimed at translating five million lines of Delphi to C#. To manage this scale, the source codebase is divided into translatable chunks, which are processed independently through an agentic LLM pipeline, with subsequently the reassembly of the translated code chunks in the target language. Due to the inherent stochasticity of LLMs, the framework incorporates processing and validation functions to ensure consistency and correctness. As an extended version of our short paper (Bruneel et al., 2025), this paper provides a significantly more comprehensive architectural blueprint. Specifically, we present an indepth background & related work section, formalise the chunking and reassembly strategies, detail the internal mechanics of agent stages, and introduce sequential agent orchestration alongside the dynamic LLM pipeline architecture. Furthermore, we report early empirical observations regarding the pipeline's computational overhead, validation dynamics, and translation quality, noting a practical impact of an initial 10x acceleration over estimated manual translation efforts. Ultimately, we share these expanded insights to inform future development and application in similar large-scale translation tasks.