AI agents for supply chain data, by what they query, produce and require. Includes what an AI agent is, the questions to ask before trusting one, an RFP generated from internal data and web research, a CO2-per-kg carrier query, and the data quality agents depend on, explained by an intern who spent six months building them.
Hi, I'm Gabriel, an intern at TetriXX AI. For the past six months, I've had the opportunity to work on developing reliable AI Agents tailored for supply chain data. This wasn't a straightforward journey as we faced many challenges in the process. The field of AI agents, especially for niche industry applications, is still quite new, and establishing a standard approach required a lot of testing and iteration. What began as an initial exploration of a subject we'd only heard about quickly transformed into the actual construction of a new solution. I'm here to share what we've learned about the way AI fundamentally changed our conception of work – both in using AI tools in the company and in building one.
Table of Contents
- What is an AI agent for supply chain data?
- What should you ask before trusting an AI agent with your data?
- How does an AI agent build an RFP from your supply chain data?
- Why does an AI agent need clean, well-structured data?
- Where do AI agents fit after code and automation?
What is an AI agent for supply chain data?
For years, many in the supply chain have relied on manual reports, vast spreadsheets, and rigid data structures to extract insights from their operations. This often involves countless hours retrieving specific information or wrestling with intricate Excel macros, frequently leading to error rates and outdated analysis. But what if there was a more direct approach? What if, instead of these traditional methods, you could simply ask a question in plain English and instantly receive precise, relevant answers? This shift is now underway with the rise of AI Agents.
In this world, where countless shipments move daily and critical information is often hidden within complex invoices, platforms like TetriXX AI already centralize your data, creating a foundational layer of efficiency. The next step leverages this foundation. As people become accustomed to interacting with general AI models for information, they learn to refine their queries for better results, making AI an increasingly valuable assistant. However, the true revolution arrives when AI itself possesses the resources to query, and intelligently chooses the correct data sources to answer your specific questions. This is the essence of AI Agents – an AI system operating under defined rules and intelligently utilizing the data resources it's given access to.
What should you ask before trusting an AI agent with your data?
Despite the growing buzz around AI Agents, it's understandable to wonder if this is merely a fleeting trend or a fundamental change in how we manage data. Is it genuinely useful, or just another technological novelty? These are crucial questions for any professional seeking reliable solutions:
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Can it provide relevant and accurate information, or simply generate a lot of aesthetically pleasing but unhelpful output?
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Can it consistently deliver reliable answers, or does the output vary unexpectedly?
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How does this approach truly differ from a general chatbot like ChatGPT?
Professionals seek dependable tools that genuinely enhance their work, not complex systems that demand more effort to operate than they save.
How does an AI agent build an RFP from your supply chain data?
Consider a practical example: the often-painful process of creating a Request For Proposal (RFP). This task demands extensive data aggregation, pulling information from various systems and numerous spreadsheets. Even with well-structured internal data, compiling the report, contextualizing performance, and researching market conditions remains a significant undertaking.
With an AI Agent, this entire process can be transformed. Instead of manual compilation, you can articulate your RFP requirements through natural language questions. The agent can then:
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Retrieve your specific internal data (e.g., historical freight costs, volume data).
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Access relevant market context by searching the web.
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Generate a complete RFP report in a desired template.
This capability stems from the custom AI Agent's access to your company's internal data, RFP templates, specific supply chain knowledge, and broader internet search functions. The power of generative AI allows for the creation of custom data and documents on demand, moving beyond the limitations of rigid, predefined pipelines.
For instance, imagine asking the agent to identify "all service providers doing more than 80 kg CO2/kg of shipments for road transportation in 2025, ranked by the number of shipments." Manually, this complex query would be a major undertaking; with an AI Agent, it's a straightforward request.
Why does an AI agent need clean, well-structured data?
It's important to clarify that AI is not a magical solution. It doesn't simply plug into your existing data and tools to magically deliver insights. An AI Agent's effectiveness is directly tied to the quality of the data it accesses and the robustness of its underlying architecture.
Think of an AI Agent as an exceptionally fast and capable worker. It can process vast amounts of information and execute complex tasks with incredible speed, but it requires clear instructions and well-organized resources. If your data is messy, imprecise, or irrelevant, the agent won't conjure brilliant insights from it. It operates on the principle of "garbage in, garbage out." The true power of these agents comes from their ability to intelligently leverage well-structured data and a robust backend system. Provide the right foundation, and they can unlock custom data insights that might otherwise remain inaccessible.
Where do AI agents fit after code and automation?
The journey towards greater efficiency and accuracy has progressed significantly with the adoption of code and automation. AI Agents represent the next, more powerful layer in this evolution. They empower supply chain professionals to achieve faster, more precise results and to uncover insights previously obscured by manual effort or rigid data structures.
As the supply chain industry stands on the brink of this conversational data revolution, what emotions does this shift evoke for you and your team – excitement, caution, or perhaps a desire to understand where to begin?
