Agentic coding and the future of supply chain leadership

Competitive advantage in supply chain may no longer come only from buying better software. It may come from leaders who can prototype the software and decision systems they wish they had.

From business requirements to working prototypes

There was a time when knowing Excel was a differentiator for supply chain professionals. Today, pivot tables, visualizations, Solver, and macros are standard business school curriculum. Excel is simply a tool of the trade and background infrastructure in supply chain. Few people understand the internal calculation engine that executes when a user types “=SUM(A1:A4)” in cell A5 and presses return. In fact, they do not need to. Excel became powerful because it let business users create useful logic without becoming software engineers.

Coding agents may do something similar for software and decision system prototypes.

Every enterprise system hides a “post-return chain of events”: the business logic, integrations, calculations, approvals and workflows triggered when a user clicks, enters, submits or approves something. In supply chain, those hidden chains process purchase and sales orders, forecast demand, plan replenishment, manage inventory levels, optimize routes, allocate slotting capacity, and design distribution networks. Users of ERP, CRM, TMS, or WMS usually do not see those chains of events. They simply get a hopefully correct output.

In most organizations, however, that invisible chain represents years of engineering effort, integration decisions, and embedded business logic. Historically, changing it required formal requirements, cross-functional alignment, development resources, validation, and time.

AI coding agents such as Anthropic’s Claude Code or OpenAI’s Codex alter this structure. They reduce the cost of turning domain expertise into working software prototypes. Someone who could not code “Hello World” can now produce a working prototype persuasive enough to test, critique, and hand to technical teams.

This is the opportunity and the risk. Coding agents allow business users to draft their own “post-return chain of events.” This is a major shift because the hidden logic of operations can now begin as a managerial draft rather than an IT request. But those users may not understand testing standards, software architecture, cybersecurity, integration requirements, or long-term maintainability. They may know exactly what the business needs, while still not knowing what the software requires.

And that’s okay. Not every supply chain leader needs to become a software engineer. The more important question is: What is the value of domain experts being able to prototype the systems they wish they had?

Consider a seasoned operations and planning executive who wants a dashboard to monitor inbound purchase orders, flag late shipments, explain the inventory impact, and recommend whether the team should expedite, substitute, reallocate, or wait. Traditionally, building such a dashboard could take weeks or months. The process might include multiple meetings to align expectations, approve design mockups, translate business needs into technical specifications, and iterate toward a deliverable. Meanwhile, the executive does not want another meeting. They just want to see whether the idea works.

Coding agents create another option: the executive can prototype it.

Why domain expertise matters

This creates a useful paradox. A supply chain executive may not understand the technical details of the system, let alone best design practices, internal software policies, or cybersecurity requirements. But they do understand the business problem, the operational tradeoffs, the decision context, and the workflow. Coding agents make it possible to convert that knowledge into a Minimum Viable Product (MVP) grounded in domain expertise.

The workflow might look like this. First, the executive instructs an AI assistant: “Interview me as if you are a software developer helping me build this solution, and produce a Product Requirements Document (PRD).” The assistant asks structured questions about functional requirements, data inputs, user interface expectations, performance constraints, and users. The executive then gives the resulting PRD to a coding agent such as Claude Code or Codex in a sandbox environment using synthetic data, approved samples or properly governed internal data. The coding agent writes the code, asks clarifying questions along the way, and produces an MVP that reflects the executive’s intent.

The difference between MVPs and enterprise systems

Coding agents do not eliminate engineering work. They change where the engineering work begins.

An MVP produced in this manner is not a production-ready system. Code generated by an AI agent must be verified, audited, secured, and aligned with enterprise architecture standards, integration protocols, and internal policies. The prototype may have business validity before it has technical validity. Even if the coding agent has access to the organization’s technical documentation, professional review is non-negotiable. Developers and engineers remain essential, but their role shifts. Instead of translating abstract requests into possible systems, they can begin with concrete prototypes and focus on hardening, integration, scalability, and deployment.

Building a culture of experimentation

There is an additional important implication: the premium on rigorous experimentation increases.

When the cost of building prototypes drops, the temptation is to deploy quickly. This is a mistake and precisely where leaders should slow down the process. A prototype that recommends expediting freight should be shadow-tested before it changes carrier spend. A replenishment rule should be simulated against demand volatility, service levels, and stockout risk before it touches inventory policy. A routing heuristic drafted with an AI agent should be compared against existing planning logic before it changes delivery commitments. These tools should be treated as hypotheses, not complete and validated systems.

Leaders should define performance metrics in advance, compare outcomes against existing baselines, and conduct controlled pilots where possible. Shadow testing new logic alongside current processes, simulating disruption scenarios and documenting where new prototypes fail becomes even more important. Agentic coding accelerates idea generation, but managerial discipline determines which ideas should scale.

Taken together, coding agents shift the boundary between domain expert and software creation. System design does not have to begin only with a formal request to IT. It can begin with a domain expert drafting, testing, and refining a working prototype. For some organizations, that will feel unsettling. For others, it will be empowering.

The strategic questions may not change: What should we buy, make, move, store, allocate, or promise? What changes is how quickly leaders can turn those questions into working systems that can be tested. To leverage these new capabilities, supply chain executives must move beyond describing ideal systems and begin drafting them.

Supply chain leaders can start today with three actions. Identify one recurring decision that is managed through spreadsheets, email or manual judgement. Convert that decision into a plain language requirements document. Then build and test a sandbox prototype against historical cases before asking IT to industrialize it.

The next generation of supply chain leaders will not be distinguished by their ability to bypass technical teams. They will be distinguished by their ability to turn operational judgment into prototypes, test those prototypes rigorously, and refine the best ideas with technical teams.

Competitive advantage will belong to those who can prototype, test, and industrialize responsibly.

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