Building an Enterprise Agentic AI

Beyond “vibe coding”, there is a structured framework that can be adopted to guide the development of an Agentic AI, Agentic AI Assistant, or Chatbot. This roadmap- the Gen AI Maturity Model can be leveraged to build an enterprise agentic AI that is auditable, relevant, scalable, resilient, truthful, and fully compliant with industry regulations. The Gen AI Maturity Model is a testament to Google’s ingenuity. In this report, I decompose the 7-step model using a hypothetical retail enterprise (e.g. Walmart) to illustrate its implementation in production. 

The objective of this report is to showcase how to use the seven steps in the Gen AI Maturity Model to build a chatbot for an enterprise retail company. The chatbot is required to be scalable, auditable, adaptive, resilient, robust, compliant, current, and free of hallucinations by adhering to the seven pillars below. 

The 7-Step Gen AI Maturity Model Roadmap: 

1. Data | 2. Model | 3. Fine-Tuning | 4. RAG Implementation | 5. Grounding | 6. Single Agent | 7. Multi-Agents

1. Data 

Data is one of the most critical aspects of this concept. Its relevance cannot be overstated and maintaining complete ownership of data- for a company like Walmart- is a massive advantage that cannot be overlooked. An enterprise company like Walmart possesses high-quality, proprietary customer data generated over the years that competitors cannot duplicate. Internally, they also have customer service interaction logs that can be used to develop a state-of-the-art (SOTA) chatbot. This data contains the specific keywords, terminology, behavior, and semantic context required for enterprise deployment. Above all, the data must be clean- meaning it is ready for consumption and free of harmful content or noise. If there are any exceptions regarding ownership, quality, domain-specificity, or cleanliness, they must be fully addressed before testing and rolling out the chatbot to production. 

2. Model Selection Strategy 

An enterprise retail company like Walmart can utilize a closed model e.g. Gemini, Claude, or Open AI, an open model e.g. DeepSeek or Kimi, an internally developed model, or a combination of these. In the absence of a proprietary internal model, my recommendation is to select a model compatible with your current cloud provider. For example, if you deploy resources in AWS or Azure, use Claude or Open AI rather than Gemini. Conversely, if your infrastructure lives in a Google environment, opt for Gemini. Choosing a model based on existing infrastructure simplifies implementation, as cloud providers natively support specific model ecosystems.  

Because closed models offer high quality, vast context windows, and robust security controls, the ultimate deciding factors are cost, hosting preference, and existing infrastructure. Considering Walmart avoids AWS due to competitive retail overlaps, Azure serves as its primary cloud anchor. Therefore, I recommend OpenAI models as the primary choice for Walmart’s chatbot development, given OpenAI’s core integration within Azure. 

3. Fine-Tuning Architecture 

To ensure the chatbot reflects Walmart’s distinct customer service tone- transforming it into a specialized agentic assistant that responds with brand-specific terms and context, I recommend leveraging LoRA (Low-Rank Adaptation) over FFT (Full Fine-Tuning) or QLoRA (Quantized Low-Rank Adaptation). Evaluating the trade-offs supports this choice: LoRA does not completely overwrite base parameters (which prevents erasing the foundation model’s general intelligence), while Walmart’s resources allow for higher sampling sizes than QLoRA requires. 

4. RAG (Retrieval-Augmented Generation): 

For an enterprise chatbot, content must remain up to date, contextually relevant, and accurate at all times. A RAG pipeline is essential for achieving this. Daily updates covering internal operations, promotional pricing, and product catalog changes can be ingested via RAG. This guarantees that every customer's response is grounded in real-time information. 

5. Grounding and External Knowledge 

A chatbot must be capable of providing truthful information even when dealing with queries outside its internal datasets. Integrating web or enterprise grounding addresses this gap. When a prompt requires external information not found within internal RAG stores, the system defers to grounding mechanisms. The synthesis of RAG and web/enterprise grounding ensures that subsequent responses remain reliable and accurate. 

6. Single Agent Evaluation 

A single agent setup is insufficient for a company of Walmart’s scale. Managing dozens of departments, millions of products, and high request volumes using a single agent creates a severe bottleneck that is neither scalable nor resilient. Therefore, relying on a single agent is not recommended for an enterprise chatbot handling high traffic. 

7. Multi-Agent Ecosystem 

Enterprise deployments require multi-agent architecture. While these architectures can be centralized/hierarchical or decentralized/flat, I recommend a decentralized, flat architecture for an enterprise like Walmart. This approach eliminates single points of failure, scales seamlessly, and remains highly adaptive. To maintain manageability, it is best practice to cap applications at roughly 20 agents. Since Walmart has dozens of departments, operations should be grouped logically into no more than 20 specialized agents. Key patterns to integrate within this multi-agent framework includes self-reflection loops, knowledge graphs, agent negotiation, conflict resolution, and multi-modality. When a customer submits a request, the network dynamically routes the query to the appropriate specialized agent. 

Conclusion and Governance 

An enterprise retailer cannot rely on a single agent architecture for customer service operations. Furthermore, comprehensive guardrails are mandatory to prevent bias, block harmful content, and route edge cases to human-in-the-loop (HITL) workflows. The inputs must be sanitized and validated; the environment secured, and post-implementation evaluation frameworks- including human annotation, RLHF, and LLM-as-a-judge- must be actively maintained to verify performance over time.