From Vibes to Context

What Truly Drives Results for Retailers When Considering Agentic AI

In the ever-evolving landscape of retail, staying ahead of the curve is not just a competitive advantage; it's a necessity. As technology continues to advance, so must our strategies for leveraging it. One of the most significant shifts in recent years has been the move from "vibe coding" to "context engineering." This transition is not merely a technical adjustment; it is a strategic transformation that can position your business for sustained success.

While every consultant will sell prompt engineering workshops, true retail partners will obsess over contextual precision engineering – the true differentiator when LLMs become commoditized.

The Allure of Vibe Coding 

Vibe coding, a term popularized by Andrej Karpathy, ex Tesla and OpenAI Co-founder, promised a new era of programming where developers could generate functional code with minimal effort. The concept was straightforward: drop a natural language prompt into an AI coding assistant, and out comes working code. This approach was particularly appealing for quick prototypes and weekend hacks, offering an immediate "dopamine hit" of seeing AI produce code rapidly and effortlessly.

However, as retail businesses began to explore the potential of AI in their operations, the limitations of vibe coding became evident. While it was effective for small, isolated tasks, it fell short when it came to building robust, scalable systems. The lack of structure and context led to technical debt, security vulnerabilities, and maintenance challenges. Teams found themselves cleaning up code that initially looked fine but failed under real-world pressure, revealing missing authorization checks, inconsistent naming, and tangled business logic.

Yet I am still hearing the promise of Vibe Coding at every vendor sponsor driven retail event in Australia! Usually proclaimed by non technology so called AI experts. Context engineering directly addresses retail's nightmare scenarios – inventory AI hallucinating 10k phantom SKUs during peak; loyalty bots leaking cross-brand partner promotions, or checkout agents failing regional compliance checks

The Rise of Context Engineering

The limitations of vibe coding have given rise to a new approach: context engineering. This method recognizes that context is a critical bottleneck in AI systems. It's not just about making code; it's about making sure the AI has all the information it needs to make good, reliable code that fits with your business goals.

Stan Polu, the founder of Dust, highlighted this shift on X (formerly Twitter) in April: "The current limiting factor in agentic intelligence is context. Context is complementary to agents and required for them to be used with any relevancy, but it becomes the bottleneck when agents' capabilities are high but context is lacking."

Context engineering is a systematic approach to managing the information that AI coding assistants need to succeed. It's like writing a detailed screenplay with all the necessary details rather than hoping the AI will improvise correctly. This approach includes four key pillars:

1. Context Capture: Creating long-term information stores that keep track of important details of your business operations, like product details, customer data, and rules.

2. Selecting Context: Pulling relevant information at the right time to ensure the AI has the necessary data to make informed decisions.

3. Compressing Context: Managing cost blow-outs with "tokens" through summarization techniques to keep the AI's context window efficient and effective.

4. Isolating Context: Structuring information to prevent noise and ensure that the AI can perform tasks with precision and accuracy.

The Shift from Vibes to Context = real impact for Retailers

For retail business leaders, moving beyond vibe coding to context engineering is more than just a technical upgrade. It is a strategic move to embrace partners and internal builds that lead with context engineering to fundamentally transform how your business operates and innovates. Here’s why:

1. Risk Reduction: Context engineering reduces the risk of technical debt and security vulnerabilities. By providing comprehensive, structured input, you ensure that the AI-generated code adheres to your business's standards and best practices. This means fewer bugs, better security, and more reliable systems.

2. Avoiding Vibe Burnout: Unlike vibe coding, which breaks down as projects get more complex, context engineering lets AI systems make good code that can grow quickly. This is crucial in retail, where systems must handle large volumes of data and transactions seamlessly.

3. Improving Consistency: Context engineering ensures that AI follows project patterns and conventions consistently across different features and team members. This consistency is important for keeping a smooth and efficient development process, which is important for fast innovation and deployment.

4. Fostering Collaboration: The structured approach of context engineering facilitates better collaboration between technical, and non-technical teams. By codifying rules and specifications in a clear, accessible format, you can ensure that everyone is on the same page, from product managers to legal advisors.

The Role of a Product Company in the Age of AI

During my recent trip to the US, I had the opportunity to present to a West Coast based VC's Startups and their investors to respond to the question “If companies can build internal tools faster with vibe coding + Agentic AI, what is the role of a product company?" This question is particularly relevant for both startups and retail business leaders who are navigating the rapidly changing landscape of AI-assisted development.

The answer is clear: the role of a product company, with deep domain or vertical expertise, in the age of AI is more critical than ever. Here are the six key points I emphasized in my presentation:

1. Domain Expertise and Context: Product companies bring deep domain expertise and a comprehensive understanding of the retail industry. This information is crucial for making AI-generated tools that work well and are based on the specific needs and challenges of retail operations. In a world where context is king, product companies are the ones who can provide the nuanced, industry-specific insights that AI needs to excel.

2. Proven Track Record: Look for a partner with a proven track record of successful AI implementations in retail. Case studies and testimonials from other retail businesses can provide valuable insights into the partner’s capabilities and the real-world impact of their solutions.

3. Customization and Flexibility: The right partner will offer customization and flexibility to tailor AI solutions to your unique processes and workflows. They will work closely with your teams to understand your specific needs and develop tools that fit seamlessly into your existing systems.

4. Reliability and Maintenance: A reliable partner will keep your AI-generated tools working and safe over time. They will have dedicated teams to address any issues that arise and to continuously improve the tools based on your feedback and evolving business needs.

5. Strategic Vision and Innovation: Choose a partner that shares your strategic vision for the future of retail. They should be focused on staying up-to-date with industry trends and new ideas. This will help you stay ahead of the competition and be the first to leverage new technologies.

6. Collaborative Approach: The right partner will foster a collaborative approach, working closely with both your technical, and non-technical teams. They will ensure that everyone is on the same page and that the AI-generated tools meet all the necessary standards and specifications.

Real-World Examples- Context led AI, without disrupting existing systems

To understand the practical implications of context engineering, let’s look at how some leading Retailers are implementing it, without disrupting existing systems:

• Walmart: Integrated shelf-scanning robots powered by AI to monitor inventory levels. These robots work with employees and connect through APIs to old stock management systems, making it easy to share data without having to change the infrastructure. They also use edge computing to process and store data locally for real-time insights.

• Sephora: Uses AI analysis of customer reviews and facial recognition tools to enhance product recommendations and store layouts. These tools were incrementally layered atop existing point-of-sale and CRM systems, allowing for data enhancements but no major system replacements.

• Target: Implemented an AI-driven inventory management system ("Inventory Ledger") that integrates with its existing store and warehouse operations. Target uses IoT sensors and machine learning to connect to its existing ERP software, automating real-time inventory with little change to the design. Over 2,000 stores use this system with old technology still in place.

• Levi Strauss: Deployed AI for demand forecasting by incrementally integrating analytics modules that connect with current inventory and sales platforms. This allowed them to boost efficiency and adapt quickly, all without ripping out older systems.

• Starbucks: Leveraged AI for its mobile rewards and personalization engine. By embedding AI into their mobile and loyalty platforms—already in use—Starbucks augmented, rather than replaced, its infrastructure, driving repeat visits and dynamic customer engagement.

In all these cases, AI was added through cloud APIs or edge computing. This increased capabilities without requiring big changes or replacement of main business systems. This approach accelerates innovation while preserving daily retail operations.

The age of vibe coding is coming to an end, and the future belongs to context engineering. For retail business leaders, this shift is an opportunity to gain a competitive edge by leveraging AI in a structured, systematic way. While your competitors and startups may still believe in the allure of vibe coding, you can soar by marrying the domain knowledge of your business operations with the technical prowess of your engineering teams. Context engineering is the key to using AI in retail. It ensures your systems are reliable, can grow, and are in line with your business goals. Embrace this transformation, and you will not only survive but thrive in the AI-driven future of retail.