Everyone’s talking about AI-generated code.
With generative AI entering the software lifecycle, code can now be produced, refactored and deployed in seconds. Various models and agents contribute to different layers of the stack, with some embedded in development tools, others operating through APIs, orchestration engines or even autonomous workflows.
But when software evolves beyond direct human control, the codebase itself becomes a dynamic system. And while the spotlight is on how game-changing this shift is, what often gets overlooked are the structural and governance implications that follow.
We were lucky enough to speak with Angelo Andreetto, Head of Data at Gunvor Group, who explains that in a world of AI-generated code, there’s no room for weak architecture or guesswork in how systems are built and governed.
As an industry, we now treat data as the critical asset it is. We’ve learned the importance of continuous monitoring, tracking for drift, decay or misalignment with reference sources, and have established the necessary controls, traceability and data quality thresholds.
We must now apply the same mindset to the codebase.
In commodities trading, your codebase is more than just infrastructure. It is a strategic asset. It encodes trading strategies, forecasting models and execution workflows, and it reflects how your firm prices, values and manages risk. It also carries both competitive advantage and regulatory accountability. Like any critical asset, it must be governed, versioned and protected.
Architecture enables this. It provides the structure needed to keep systems stable under pressure and manage growing complexity. More than just technical scaffolding, software architecture defines how policy, accountability and strategy are embedded in code. This is why architects play a critical role as the custodians of the intellectual property within the codebase.
Without robust and scalable architectural frameworks, firms risk losing transparency, reproducibility, and trust in their own systems. Architecture becomes the backbone of responsibility and the enforcer of accountability.
As code automation and AI adoption accelerate, four dimensions now demand attention:
- Segment the Software Development Life Cycle by function and risk class: Define which phases are eligible for AI augmentation, to what extent, and under what conditions.
- Apply continuous validation to all AI-generated logic: Capture lineage, enforce boundaries and monitor for structural or behavioural drift.
- Reinforce architectural governance: Maintain traceability, consistency and explainability, especially where regulatory scrutiny applies.
- Embrace functional redundancy and operational agility: Allow the governed proliferation of coding assistants and let engineers justify and adopt the ones they find most useful.
In commodities trading, we must be able to explain what our systems do, how and why they did it, and whether they stayed within the intended limits.
Speed and scale matter. But in high-value environments, they must be matched by clarity, discipline and control.
Because acceleration without direction is not just inefficient. It is costly.
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